Anonymous communication networks have emerged as crucial tools for obfuscating communication pathways and concealing user identities. However, their practical deployment faces several critical challenges, including vulnerability to artificial intelligence-powered metadata analysis, difficulties in fitting decentralized architectures, and the lack of provable security guarantees. To address these limitations, this paper proposes a novel decentralized anonymous routing protocol that resists both traceability and traffic analysis. The proposed protocol is fully decentralized and eliminates reliance on the threshold model and trusted third-party setups, ensuring indistinguishable identity privacy. Different from traditional empirical or heuristic security analysis of anonymous networks, this paper rigorously proves indistinguishable identity privacy for users even in extremely adversarial environments. Furthermore, simulation results validate the protocol’s practical feasibility, demonstrating both security and efficiency. By enabling secure information exchange while preserving user privacy, the proposed protocol offers a provably secure solution for privacy-preserving communication in modern digital infrastructures.

The coverage optimization for Unmanned Aerial Vehicles (UAVs) in low-altitude intelligent networks (LAINs) poses a significant challenge due to factors such as multiple performance metrics, large-scale systems, limited global information, and potential UAV failures. To address these challenges, this paper proposes an optimal control-based framework to model the joint coverage optimization problem, which comprehensively extends system endurance, maximizes throughput, enhances global connectivity, and ensures ground user fairness in dynamic scenarios. To solve this problem, we develop a fully distributed algorithm for UAVs’ navigation and real-time adjustments of Common Pilot Channel (CPICH) transmit power. Owing to its distributed nature and ability to maintain global connectivity, the algorithm ensures robust system performance in uncertain environments. It offers advantages such as low computational complexity, strong scalability, and efficient handling of node failures, demonstrating resilience and rapid self-recovery capabilities. To validate its effectiveness, we conduct simulations in large-scale scenarios. The results confirm that the proposed algorithm achieves efficient and fast-response performance with quick self-recovery.

The paradigm of integrated sensing and communication (ISAC) is envisioned as a key enabler for the evolution of 6G, leveraging inherent similarities of dual functions in hardware architectures and signal processing to sense the environment and send messages via a shared waveform. In this paper, we establish a theoretical framework to evaluate the sensing and communication (S&C) performance of bistatic ISAC systems under Gaussian fading channels at finite blocklength, where a primary focus lies in uncovering the fundamental tradeoff between dual functions due to limited resources. In particular, we first formulate the joint S&C problem in bistatic single-input and single-output (SISO) ISAC systems, and define the rate-error tradeoff to quantify the performance balance between S&C. Then we derive the achievability and converse bounds for the rate-error tradeoff, providing a deep comprehension of the interplay between S&C functions. Finally, we discuss the extensions of our framework involving infinite blocklength regime, general parameter estimation and multiple-input and multiple-output (MIMO) channel.

Deep learning based methods have been increasingly applied to wireless localization and sensing. However, many existing methods suffer from poor generalization, which limits their adaptability to unseen environments. Additionally, retraining these models demands a substantial amount of labeled data, which is both time-consuming and labor-intensive. To address these challenges, we propose GenLoc, a generalization framework that enables accurate mitigation of ranging and angle estimation errors in unseen environments via feature disentanglement, without the need for additional data collection and online training. The key lies in learning fine-grained domain-invariant representations that are first extracted by minimizing feature distribution discrepancies across various domains and then refined through domain-invariant and domain-specific feature decoupling. Extensive experiments were conducted on real-world datasets, covering five distinct scenarios, each with five different obstacles, and a thorough comparison with several existing approaches illustrates that GenLoc improves average distance and angle estimation accuracy by more than 22% and 28%, respectively, while others exhibit negligible improvements or significant degradation.

In multi-agent systems, the hybrid active-silent relative localization framework is widely employed, where only active nodes transmit signals. The selection of active nodes, known as node activation, significantly impacts the positioning accuracy. This paper investigates the node activation in anchor-free localization systems. First, the constrained Cramér-Rao lower bound (CRLB) is derived to evaluate the localization error. Then the combinatorial optimization problem on node activation is presented and approximately solved using the difference of convex programming (DCP) method. Moreover, to reduce computational complexity, we propose a geometry-based greedy iterative (GBGI) algorithm which leverages a geometry metric to evaluate and iteratively refine the selection of active nodes. Finally, simulation results demonstrate the performance of proposed algorithms. Especially the GBGI algorithm closely approaches the optimal solution.

This paper investigates a bi-static integrated sensing and communication (ISAC) system for multi-target scenarios using impulse radio ultra-wideband (IR-UWB) signals, which offer fine temporal resolution, low power consumption, and strong resistance to multipath interference. Two typical modulation schemes, namely pulse position modulation (PPM) and binary phase shift keying (BPSK), are considered for communication over the delay and phase domains, respectively. An innovative differential decoupling strategy is proposed, which eliminates the need for pilot symbols by leveraging the known starting symbol position. The sensing performance under various modulation and demodulation schemes is analyzed and compared with the conventional pilot-based (time-delay) decoupling strategy under current UWB standards. A key contribution of this work is the development of a unified analytical framework based on the Fisher information matrix (FIM), which characterizes the fundamental coupling between communication and sensing in both delay and Doppler domains. This coupling is examined through the singularity structure of the FIM, providing new insights into the joint performance limits of UWB-ISAC systems. Performance evaluation is conducted using the Cramer-Rao Lower Bound (CRLB) for sensing and the data transmission rate for communication, offering theoretical insights into choosing suitable data signal processing methods in real-world applications.

Existing integrated sensing and communication technology designed for monostatic settings segregates sensing and communication (S&C) at distinct terminals, impeding their adaptability to network paradigms. In this paper, we propose a general framework for joint target-localization and data-detection (JTD) in bistatic settings by using a combination of the deterministic known (DK) and random unknown (RU) symbols, e.g., pilot and data symbols. We first derive the performance limits for target localization-related parameters with combined symbols. We demonstrate that the Fisher information matrix (FIM) for target localization using the DK part can be expressed in a closed form through orthogonal projection, while that using the RU part aligns with the modified FIM in monostatic settings at an exponential rate with respect to the signal-to-noise ratio. Then, we propose a JTD scheme to exploit the reciprocal advantages for S&C, i.e., the detected data symbols are harnessed to enhance target localization in an alternating way. Finally, simulation results validate our theoretical analysis and the effectiveness of the proposed JTD scheme.

Ultra-wideband (UWB) is a prominent technology for wireless localization, mainly attributed to its superior ranging performance enabled by the large signal bandwidth. However, its bearing capability remains underdeveloped due to practical issues such as phase deviations, antenna coupling, and phase ambiguity. This paper presents a high-accuracy stereo UWB bearing scheme through network ambiguity resolution and online phase calibration. Specifically, we propose a sparse variational Gaussian process regression-based calibration technique to eliminate phase deviations and a range-assisted network solution to resolve phase ambiguities. Building on these techniques, we present an online angle estimation scheme that performs real-time phase calibration, ambiguity resolution, and calibration model updates, significantly reducing calibration complexity in large-scale networks. Real-world experiments on 4-element stereo UWB platforms achieve root mean square errors of 2.3$^{\circ }$and 1.1$^{\circ }$for azimuth and elevation angles, respectively. The success rate for ambiguity resolution exceeds 96%, a 20% improvement over existing methods.

Communication in multi-agent reinforcement learning (MARL) has been proven to effectively promote cooperation among agents recently. Since communication in real-world scenarios is vulnerable to noises and adversarial attacks, it is crucial to develop robust communicative MARL technique. However, existing research in this domain has predominantly focused on passive defense strategies, where agents receive all messages equally, making it hard to balance performance and robustness. We propose an active defense strategy, where agents automatically reduce the impact of potentially harmful messages on the final decision. There are two challenges to implement this strategy, that are defining unreliable messages and adjusting the unreliable messages' impact on the final decision properly. To address them, we design an Active Defense Multi-Agent Communication framework (ADMAC), which estimates the reliability of received messages and adjusts their impact on the final decision accordingly with the help of a decomposable decision structure. The superiority of ADMAC over existing methods is validated by experiments in three communication-critical tasks under four types of attacks.

Tracking the kinetic state of a non-cooperative target in time-varying non-line-of-sight (NLOS) environment is a challenging problem in many applications. The distance estimation (DE) of a target in such tracking system, produced by local anchors, can be influenced by a positive bias caused by refraction and reflection in the physical channel within a NLOS environment. Specifically, in a DE-based positioning network, such as a Time Difference of Arrival (TDOA) localization system with several listening anchors blocked, the NLOS error can cause an overall deviation in positioning, which in turn affects the accuracy of trajectory estimations. In this paper, we develop a regression-based error transition model to associate the NLOS errors with the target states by its previous trajectory. Then, by employing a joint variational Bayesian (VB) approximation, we decouple this association iteratively through algorithm. Thereby facilitating an accurate estimation of the posterior distribution for target trajectories and the ranging error. Experiments involving an actual Unmanned Aerial Vehicle (UAV) and TDOA localization system validate the robustness and performance of our algorithm compared to existing approaches.

Cryo-electron microscopy (Cryo-EM) is a revolutionizing technique that facilitates the determination of macromolecule structures. To attain high-resolution structures, numerous noisy images should be iteratively aligned and reconstructed into 3D models. The alignment step is the bottleneck of computational efficiency in the existing frameworks. In this paper, we indicate the log-likelihood (LLH) function for alignment can be converted to a concise form through proper domain transformation. Based on this conversion and the corresponding fast transformation algorithms, a fast and precise alignment algorithm is proposed. The algorithm reduces the computational complexity from $\mathcal{O}({N^7})$ to $\mathcal{O}({N^5}\log N)$ (where N is the sample points in each dimension). The efficiency and correctness of the proposed algorithm are validated by the experiments on both synthetic and real datasets.

Multi-agent sensing systems are gaining widespread deployment due to their dynamic flexibility and collaborative benefits. Nonetheless, the sensing task's dependence on localization and formation results in a decline in sensing accuracy due to errors in these two aspects. In this paper, we provide a comprehensive analysis of the error coupling among localization, formation, and sensing. By utilizing Fisher information, we evaluate the influence of measurement and control noises on sensing accuracy, enabling us to determine an objective function for sensing under wireless resource constraints. Then we propose an iterative optimization algorithm for control strategy and resource allocation, which effectively improves sensing accuracy by minimizing the effects of localization and control errors on sensing. Simulation results demonstrate the significant performance improvements achieved by our proposed joint optimization.

Tilt-series alignment in cryo-electron tomography (cryo-ET) data processing is essential for visualizing high-quality structural information of macromolecules and organelles. Tilt-series alignment requires feature tracking, while the existing methods have challenges in identifying features in different kinds of specimens under a unified framework. In this paper, we propose an adaptive tilt-series alignment algorithm based on automatic feature seeking. With feature resampling based on local correlation coefficients, the proposed method can track distinguishable features in different kinds of specimens automatically. Experimental results show that the proposed method outperforms the existing methods on conventional specimens and yields comparable results on specimens with strong prior information from gold beads.

Ultra-wide band (UWB) systems are gaining popularity for multi-robot localization benefiting from their high-accuracy ranging capabilities. However, current UWB systems fall short in determining orientations and realizing pair-wise localization for neglecting bearing information. Given the importance of bearing capabilities, especially when vision-based methods fail, this paper proposes a high-accuracy 2-D bearing estimation method using stereo UWB arrays. We propose a novel phase error calibration method that effectively mitigates various phase imperfections. This array is designed with antenna spacing larger than half the wavelength to diminish antenna coupling and enhance bearing accuracy. As regards the phase ambiguity issue arising from large antenna spacing, a distributed range-assisted phase ambiguity determination method is developed. Our bearing estimation method exhibits low complexity and is well-suited for the deployment on mobile robots with limited computational resources. The performance of the proposed method is validated on the practical platforms under dynamic scenarios, yielding root mean squared errors (RMSEs) less than 4° and 3° for azimuth and elevation angle estimation, respectively.

Ultra-wideband (UWB) is a popular technology for robotic localization in global positioning system (GPS)-challenged and vision-obstructed scenarios. In UWB localization systems, distance information is extracted from ToA and ToD timestamp measurements. However, these measurements are easily influenced by hardware limitations and complex propagation environments, making an effective calibration method crucial for achieving high-accuracy ranging. This paper proposes a cooperative timestamp calibration method, which effectively mitigates ranging errors with scalability, adaptability, and flexibility. Our approach reduces the calibration complexity from O(N2) to O(N) for networks within N nodes and allows for distributed implementation to lower communication costs. The enabler is developing a new timestamp measurement model that can rectify all timestamps across different devices in a unified manner, coupled with the introduction of cooperative model training techniques that accommodate both feasible and infeasible scenarios for precisely labeling node positions. Real-world experimental results show that our method reduces the ranging error from 38.02 cm to 8.17 cm within a fully labeled 4-node network and from 16.77 cm to 9.61 cm in an 8-node network without labeling.

Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.

Time-sparse frequency-hopping (FH) signals detection and sequences identification present a significant challenge in spectrum tracking of multiple targets. The non-continuous observations that arise from their temporal sparsity complicates identification efforts in low signal-to-noise ratio (SNR) environments. In this letter, a dynamic temporal perception probability hypothesis density (DTP-PHD) filter for spectrum tracking of multiple targets with potential periodicity was proposed by leveraging the periodicity alignment likelihood ratio (PALR). The PALR enables the estimation of the time transition function of targets' FH signals, which also facilitates the extraction of the spectrum track by identifying each target using a joint posterior intensity. Moreover, a closed-form solution of DTP-PHD was derived under linear Gaussian assumptions. The validity of the periodicity estimation was established by implementing a particle version of the proposed algorithm, which demonstrated robust tracking performance in noisy environments.

Exploring the relative positions is a key issue in many emerging location-aware applications such as autonomous driving and formation control, where there exists no infrastructure to provide the absolute position information. In this paper, we establish a theoretical framework to address the state estimation problems in relative localization networks. In particular, we introduce the relative error for state estimates based on the concept of the equivalent state class, and apply the Fisher information analysis to derive the performance bounds. Then we present how measurement uncertainties influence the performance limits in the relative localization networks with self-measurements, after which our framework is extended to the scenarios with clock asynchronization and temporal cooperation. Finally, the connection between the theoretical foundation and the algorithm design is illustrated to provide insights into the operations in practical relative localization networks.

Multi-agent sensing for transportation systems is receiving widespread attention due to its dynamic flexibility and collaborative capabilities, where the target sensing error is limited by the spatio-temporal error caused by agent localization and formation steps. This paper considers the sensing problem of non-cooperative targets (UAVs or vehicles) by cooperative asynchronous agents (UAVs). This paper develops a framework where the formation of agents and the allocation of resources are jointly optimized. In particular, we reveal the error coupling of measurement and motion noises on target sensing accuracy by Fisher information analysis. Then we propose bandwidth allocation and agent activation strategies in the localization step, which simultaneously improve the position accuracy of agents and the quality of sensing signals. In the formation step, we design motion planning algorithms to increase sensing information about targets. Simulation results demonstrate the significant performance improvements achieved by our proposed algorithms that minimize the effects of localization and control errors on target sensing.

Cooperation among entities within networks for information exchange and measurement is a promising paradigm for high-accuracy positioning in automated vehicles. However, due to imperfect clocks and inefficient wireless protocols, current cooperative positioning techniques have inadequate accuracy and timeliness. This paper presents a novel localization framework for connected automated vehicles (CAVs) capable of achieving high-accuracy relative positioning with high update rates. We design a signal-multiplexing network measuring (SNM) protocol to optimize the measurement update rates and propose new range estimations to achieve high-accuracy ranging against clock errors and mobility. Using range estimations, we develop a relative localization algorithm that leverages intra- and inter-node cooperation with coordinate reference alignment to reconstruct the geometric relationships among the nodes. Performance analyses and simulation results demonstrate that our method achieves high-accuracy positioning with timely updates, ensuring reliability and robustness in asynchronous vehicle localization.

Ultra wideband (UWB) systems have received great interest again due to the high range resolution, flexible data transmission capability, and low power consumption. In this paper, we develop a practical asynchronous integrated sensing and communication (ISAC) system using impulse radio UWB signals. This system operates within a joint mono-bistatic sensing network, accommodating multiple static and dynamic targets. To achieve simultaneous communication and target sensing, a reliable soft information based decouple solution is proposed to perform data demodulation in the typical UWB modulation waveforms. The data transmission can benefit from proper channel sensing, to about 2-3 dB gain, by exploiting the multipath components for demodulation. In addition to the demodulated data bits at the bi-static receiver, the environmental target distance and Doppler shift can also be achieved at both mono- and bi-static receivers, respectively. We then evaluate the sensing capability of the ISAC UWB system, by extensive simulations and practical experiments. The target tracking accuracy can be achieved within 20 cm at over 80% confidence, with commercial UWB devices according to practical measurements.

In passive Internet of Things, existing synthetic aperture-based 3D localization methods face many challenges, such as high computational load, a large aperture of a virtual antenna array, and sensitivity to noise. To address these challenges, this paper develops a synthetic aperture scheme for integrated localization and navigation, which implements the localization algorithm with a trajectory generated by the navigation algorithm. The localization problem is formulated by multidimensional scaling, which exploits phase differences involving the spatial information between target tags and a virtual antenna array. The new formulation allows the system to provide an accurate location estimate with a large moving step and sparse virtual antenna array of narrow apertures. The navigation problem is formulated to decrease errors of distance differences. Moreover, a navigation criterion is established to determine the feasibility of a virtual antenna position based on phase measurements, and an efficient navigation algorithm is proposed to find such a feasible point. Extensive numerical results validate our theoretical analysis and the performance of the proposed scheme.

Location awareness plays an increasingly important role in wireless network applications. However, accurate localization in complex indoor environments remains challenging for existing radio frequency (RF)-based systems, among which the ultra-wide bandwidth (UWB) technology ranks to be the most promising one due to its capability in providing channel information with fine time resolution. In this paper, we propose a multipath-assisted single-anchor localization framework that can provide high-accuracy positional information in complex indoor environments. Specifically, a deep variational learning method is proposed to produce calibrated estimates of position-related parameters, including distance, time-difference-of-arrival and angle-of-arrival, which are then fed into a multipath-assisted single-anchor localization algorithm. The proposed method is implemented on self-built UWB transceivers and assessed with real-world data from an indoor measurement campaign. Extensive experimental results show that the proposed method outperforms conventional machine learning-based error mitigation approaches and can achieve 0.15m root mean square position error in non-line-of-sight scenarios.

In this paper, we construct a totally asynchronous integrated sensing and communication (ISAC) system using impulse radio ultra-wideband (IR-UWB) signals. In a typical bistatic network in the presence of several static and dynamic targets, both line-of-sight (LOS) and multi-path components (MPCs) are considered. The transmitted data symbols demodulation and the Doppler measurement of targets within the environment could be achieved simultaneously, using a low complexity sequential estimation algorithm. In addition, the data transmission could achieve about 1.5 dB gain from the channel sensing, due to the fact that the extra MPC energy could be collected for data demodulation in this framework.

This paper applies the exponential decay property of scalable RL theory to a specific scenario where the network structure is a tree, and use KL (Kullback-Leibler) divergence to analyze the propagation of approximation error along the structure over time, in order to quantify its backtracking result. We gain the insight that most of the approximation error originates from the inaccurate estimation of the state of the source nodes (root in Top-Down mode and leaves in Bottom-Up mode), which can be largely recovered by establishing the long-hop communication link12.

Integrated sensing and communication (ISAC) has been proposed as a promising paradigm in the future wireless networks, where the spectral and hardware resources are shared to provide a considerable performance gain. It is essential to understand how sensing and communication (S&C) influences each other to guide the practical algorithm and system design in ISAC. In this paper, we investigate the performance tradeoff between S&C in a single-input single-output (SISO) ISAC system with finite blocklength. In particular, we present the system model and the ISAC scheme, after which the rateerror tradeoff is introduced as the performance metric. Then we derive the achievability and converse bounds for the rateerror tradeoff, determining the boundary of the joint S&C performance. Furthermore, we develop the asymptotic analysis at large blocklength regime, where the performance tradeoff between S&C is proved to vanish as the blocklength tends to infinity. Finally, our theoretical analysis is consolidated by simulation results.

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This article presents TDLoc, a multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM)-based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains the Angle-of-Departure (AoD), Angle-of-Arrival (AoA), Time-of-Flight (ToF), and Doppler frequency shifts (DFSs) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér–Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.

Edge computing has been envisioned as a key enabler to provide computation-intensive and delay-sensitive services in the future Internet of Things systems. By offloading the computational tasks to the edge server, both the service latency and energy consumption can be reduced. Since devices may request various types of computing services, caching appropriate services in the edge server to immediately provide computing resources can improve the quality of service. Nevertheless, it brings new challenges to jointly optimize the resource allocation, where the timeliness of caching and offloading operations are different. In this article, we first formulate the collaborative service caching and computation offloading as a dual-timescale resource allocation problem to minimize the costs of latency and energy consumption. Under this framework, a novel scheme based on hierarchical deep reinforcement learning is proposed to output collaborative caching and computing actions. Specifically, the proposed approach contains the service caching policy and the device computing policy with hierarchical action–value functions, which allows a flexible configuration of caching timescales. The simulation results demonstrate that the proposed policy outperforms the existing schemes on convergence performance and various parameters.

Location-awareness has become a fundamental requirement for multiple emerging applications with the rapid development of wireless technologies. The high-accuracy ranging enabled by ultra-wide bandwidth (UWB) signals is often deteriorated by clocks imperfections and non-line-of-sight (NLOS) propagation. Existing supervised learning methods for NLOS identification and mitigation are time-consuming, labor-intensive, and cost-inefficient due to the need for training data acquisition and label assignment. This paper presents an indoor localization system that enables NLOS mitigation based on self-training. The system provides a general information fusion framework that integrates map, inertial sensors, and UWB measurements, where the weak labels for UWB measurements are produced and iteratively refined by multi-sensory information fusion for self-training. In addition, the system utilizes the maximum likelihood ranging estimator that considers the impact of clock drift. The effectiveness of the proposed system is demonstrated via extensive experimentation in multiple real-world environments, e.g., the proposed methods reduce the NLOS ranging error by 80% and result in a 90th localization error percentile of 0.5 meters in a complex indoor environment.

Vehicular edge computing (VEC) is an effective method to increase the computing capability of vehicles, where vehicles share their idle computing resources with each other. However, due to the high mobility of vehicles, it is challenging to design an optimal task allocation policy that adapts to the dynamic vehicular environment. Further, vehicular computation offloading often occurs between unfamiliar vehicles, how to motivate vehicles to share their computing resources while guaranteeing the reliability of resource allocation in task offloading is one main challenge. In this paper, we propose a blockchain-enabled VEC framework to ensure the reliability and efficiency of vehicle-to-vehicle (V2V) task offloading. Specifically, we develop a deep reinforcement learning (DRL)-based computation offloading scheme for the smart contract of blockchain, where task vehicles can offload part of computation-intensive tasks to neighboring vehicles. To ensure the security and reliability in task offloading, we evaluate the reliability of vehicles in resource allocation by blockchain. Moreover, we propose an enhanced consensus algorithm based on practical Byzantine fault tolerance (PBFT), and design a consensus nodes selection algorithm to improve the efficiency of consensus and motivate base stations to improve reliability in task allocation. Simulation results validate the effectiveness of our proposed scheme for blockchain-enabled VEC.

Wireless propagated signals encapsulate rich information for high-accuracy localization and environment sensing. However, the full exploitation of positional and environmental features as well as their correlation remains challenging in complex propagation environments. In this paper, we propose a methodology of variational inference over deep neural networks for concurrent distance estimation and environmental identification. The proposed approach, namely inter-instance variational auto-encoders (IIns-VAEs), conducts inference with latent variables that encapsulate information about both distance and environmental labels. A deep learning network with instance normalization is designed to approximate the inference concurrently via deep learning. We conduct extensive experiments on real-world datasets and the results show the superiority of the proposed IIns-VAE in both distance estimation and environmental identification compared to conventional approaches.

Reliable and accurate localization awareness is of great importance for the distributed multi-agent system (DMAS). Instead of global information, measurements only between neighbors pose locatability and accuracy challenges for distributed systems, which leads to the development and application of relative localization. In this paper, we put forward a backbone-listener localization scheme for the D-MAS. Agents switch backbone-listener modes through a node selection strategy. Position and orientation angle of agents are jointly estimated by range and angle information fusion. A distributed multidimensional scaling method is proposed for backbone agents to maintain the topology estimation. And listener agents ensure the localization capacity through a least square range and angle fusion algorithm. Extensive simulation and real-world experiments validate that our method achieves decimeter-level accuracy relative localization.

High-accuracy position information is essential for the emerging applications of Internet of Things, where relative localization is often more pertinent in many cooperative tasks. In this paper, we propose a distributed relative localization scheme for large-scale 3D networks where the relative position relationships of the entire network as well as the subnetwork are concerned. In particular, we first design the prioritized geometry merging procedure with merging confidence evaluation of the geometry pairs for the entire network localization. Then we specifically extend this priority-based methodology for the algorithm design of subnetwork-aimed localization. Numerical results demonstrate that the proposed schemes significantly outperform existing algorithms.

Observability is an essential aspect for the performance of a visual Simultaneous Localization and Mapping (SLAM) network. This paper presents a statistical perspective to evaluate the observability of visual SLAM networks and its dependence on network structure. In particular, we first give the general form for the Fisher information matrix (FIM) of each visual observation and the impact of 3D point translation and camera motion on observations. Then the observability of visual SLAM networks is investigated based on the nullspace of the FIM and its relation with network structure to derive the lost rank and its upper and lower bounds. We also propose the ill-conditioned score to evaluate the degradation of visual SLAM performance under any network structure. Numerical results validate our conclusions.

Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address advanced conditional generative problems without annotations, which can enable multiple applications like image-to-image translation and image editing. We present a unified Bayesian framework for such problems, which introduces an inference stage on latent variables within the learning process. In particular, we propose a variational Bayesian image translation network (VBITN) that enables multiple image translation and editing tasks. Comprehensive experiments show the effectiveness of our method on unsupervised image-to-image translation, and demonstrate the novel advanced capabilities for semantic editing and mixed domain translation.

Network localization systems based on antenna arrays provide high-accuracy positions. With the increase of antenna number, higher positioning accuracy is achievable, but more computation and communication resources are required at the same time. In order to reduce the computation and communication resource consumption, the element space signal is expected to be compressed without localization accuracy degeneration. In this paper, we consider the array signal dimension reduction for dynamic localization systems, which, to the best of our knowledge, has not been investigated before. Based on the performance limits of beamspace localization, information lossless beamspace schemes are proposed. The effects of the clock asynchronism, the Doppler shift, and the signal bandwidth on proposed schemes are presented. To deal with the parameter uncertainties, a heuristic robust method is proposed and simulation results validate the effectiveness. Our work reveals the low-dimensional property of array signals under dynamic localization scenarios.

Multi-agent systems are being widely deployed to various tasks due to excellent collaboration gains. These tasks usually contain localization and control stages, in which the information coupling mechanism is still unclear thus many system resources are wasted. In this paper, we integrally analyze the performance bound of 3D formation accuracy with localization uncertainty so as to reduce consumption. We start from the equivalence class distance to analyze the 3D relative formation distance and obtain its closed-form upper bound and lower bound. Then we establish an integrated localization and control framework for the 3D formation and analyze how observation and control errors affect the formation accuracy. We propose integrated feedback resource allocation algorithms including agents scheduling and power allocation. Simulation results show the significant performance gain and resource cost reduction brought by the integrated framework.

Source localization is essential for a wide range of applications. The efficiency of source localization in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). In this paper, we investigate the information inequality of RIS-aided localization systems. We propose a general signal model for RIS-aided localization valid for both near-field and far-field scenarios. Based on the proposed model, we perform Fisher information analyses of localization performance in networks with RISs. Numerical results show that optimal-configured RISs can improve the localization accuracy significantly.

With the emerging paradigm of Internet of Things, high-accuracy localization has been an ever-present key issue, and relative position information is getting growing attention in cooperative tasks. In this paper, we propose an efficient relative localization algorithm for three-dimensional anchor-free networks with limited communication range. Specifically, we first design the condition number-based geometry selection criteria. Then we develop a scheme to establish a well-conditioned reference coordinate system for the network. Furthermore, an iterative relative localization algorithm is proposed, in which the reliability of the agents is evaluated for dynamic virtual anchor extension. The numerical results validate that the performance gain of the proposed relative localization scheme over existing algorithms is significant.

Disaster areas involving floods and earthquakes are commonly large, with the rescue time being quite tight, suggesting multi-Unmanned Aerial Vehicles (UAV) exploration rather than employing a single UAV. For such scenarios, current UAV exploration is modeled as a Coverage Path Planning (CPP) problem to achieve full area coverage in the presence of obstacles. However, the UAV's endurance capability is limited, and the rescue time is constrained, prohibiting even multiple UAVs from completing disaster area coverage on time. Therefore, this paper defines a multi-Agent Endurance-limited CPP (MAEl-CPP) problem that is based on an a priori known heatmap of the disaster area, which affords to explore the most valuable areas under UAV limited energy constraints. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem by ranking the possible disaster areas according to their importance through satellite or remote sensing aerial images and completing path planning according to this ranking. Experimental results demonstrate that the search efficiency of the proposed algorithm is 4.2 times that of the existing algorithm.

Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop an autonomous robot exploration benchmark en-titled Explore-Bench. The benchmark involves various explo-ration scenarios and presents two types of quantitative metrics to evaluate exploration efficiency and multi-robot cooperation. Explore-Bench is extremely useful as, recently, deep rein-forcement learning (DRL) has been widely used for robot exploration tasks and achieved promising results. However, training DRL-based approaches requires large data sets, and additionally, current benchmarks rely on realistic simulators with a slow simulation speed, which is not appropriate for training exploration strategies. Hence, to support efficient DRL training and comprehensive evaluation, the suggested Explore-Bench designs a 3-level platform with a unified data flow and 12 × speed-up that includes a grid-based simulator for fast evaluation and efficient training, a realistic Gazebo simulator, and a remotely accessible robot testbed for high-accuracy tests in physical environments. The practicality of the proposed benchmark is highlighted with the application of one DRL-based and three frontier-based exploration approaches. Fur-thermore, we analyze the performance differences and provide some insights about the selection and design of exploration methods. Our benchmark is available at https://github.com/efc-robot/Explore-Bench.

Multi-agent formation as well as obstacle avoid-ance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control achieve a certain measure of success, most of them require precise global information which is not accessible in harsh environments. On the other hand, some reinforcement learning (RL) based approaches adopt the leader-follower structure to organize different agents' behaviors, which sacrifices the collaboration between agents thus suffering from bottlenecks in maneuver-ability and robustness. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). Agents in our system only utilize local and relative information to make decisions and control themselves distributively, and will reorganize themselves into a new topology quickly in case that any of them is dis-connected. Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines (both classic control methods and another RL-based method). The feasibility of our method is verified by both simulation and hardware implementation with Ackermann-steering vehicles.

The wideband system plays a important role in high-accuracy location awareness. In beyond 5G networks, reconfigurable intelligent surfaces (RISs) are proposed to control the complex wireless environments. However, existing works related to RISs mainly focus on narrow-band systems due to the frequency-selectivity of conventional metasurfaces. This paper first presents the general signal model of wideband systems with RISs, and then performs Fisher information analysis to determine the theoretical limits of wideband localization with RISs. Furthermore, special scenarios including complete coupling scenarios and complete decoupling scenarios are discussed and evaluated. The simulation result shows that RISs in complete coupling scenarios provided more improvement, in terms of squared position error bound (SPEB), compared with that in complete decoupling scenarios and random configuration.

Achieving accurate location-awareness in wireless networks requires integrated sensing and communication (ISAC), where optimization, signal processing, and data fusion are performed under a common framework. The efficiency of ISAC in complex wireless environments can be improved via the use of reconfigurable intelligent surfaces (RISs). This paper introduces the concept of continuous intelligent surface (CIS) and establishes the fundamental limits of RIS-aided ISAC systems, specifically, an RIS-aided localization and communication system. In particular, this paper considers two types of RISs, namely CISs and discrete intelligent surfaces (DISs). First, this paper proposes a general signal model for RIS-aided localization and communication valid for both near-field and far-field scenarios, and then theoretical limits on the localization and communication performance are derived. Based on the proposed model, Fisher information analyses of the localization performance in networks with RISs are performed. Numerical results show that RISs with optimized phase responses can improve the received signal-to-noise ratio (SNR) and spectral efficiency of communication, and the localization accuracy significantly.

The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for arbitrary decision problems where it corresponds to minimax approaches. This paper establishes a framework for supervised classification based on the generalized maximum entropy principle that leads to minimax risk classifiers (MRCs). We develop learning techniques that determine MRCs for general entropy functions and provide performance guarantees by means of convex optimization. In addition, we describe the relationship of the presented techniques with existing classification methods, and quantify MRCs performance in comparison with the proposed bounds and conventional methods.

Network localization is capable of providing accurate and ubiquitous position information for numerous wireless applications. This paper studies the accuracy of cooperative network localization in large-scale wireless networks. Based on a decomposition of the equivalent Fisher information matrix (EFIM), we develop a random-walk-inspired approach for the analysis of EFIM, and propose a position information routing interpretation of cooperative network localization. Using this approach, we show that in large lattice and stochastic geometric networks, when anchors are uniformly distributed, the average localization error of agents grows logarithmically with the reciprocal of anchor density in an asymptotic regime. The results are further illustrated using numerical examples.

Multi-agent scheduling problems are common in applications such as intelligent transportation and smart manufacturing. When the agents are non-cooperative and inertially constrained, finding a safe and efficient policy under the trajectory uncertainty of other agents is a non-trivial problem. In this article, we establish a minimax framework to optimize the worst-case scheduling performance under the two-agent model. Specifically, a unified representation is proposed to characterize the trajectory uncertainty of the other agent, and a function is derived to evaluate different target states. Based on this evaluation, we further develop a control policy by adopting the minimax method, where a trajectory leading to the most robust target state is generated at each step. Algorithms are also provided to ensure the computational tractability of the policy. Furthermore, the safety of the policy is proved, and the global robustness is verified by numerical simulations, which show that the proposed policy reduces the worst-case scheduling cost by 13.1% compared with heuristic policies.

Scheduling of physical space in multi-agent systems is crucial in widespread applications including transportation and industrial manufacturing. However, few existing works focused on improving the scheduling efficiency when agents are inertially constrained and some non-cooperative agents with unknown and uncontrollable trajectories exist. In this article, we establish a minimax framework aiming to ensure the robustness of scheduling against the uncertainty of non-cooperative agents. Specifically, we propose a function characterizing the preference of different states based on a given situation information, and formulate a trajectory planning policy by establishing a minimax optimization problem. Furthermore, the tractability of the proposed policy is ensured by developing an approximate algorithm and a truncation method, and the safety guarantee of the policy is also proved. Finally, numerical simulations suggest a 90% reduction on the empirical probability of high-cost scenarios compared with heuristic policies, validating the robustness of the proposed policy.

The ultra-wideband (UWB) technology has been widely used in high-accuracy localization systems benefiting from its superior time-resolution and stability. In recent years, the single-anchor localization (SAL) scheme has attracted intense interests with its high accuracy, low system complexity, and low deployment costs compared to traditional UWB localization schemes. In this paper, we develop a SAL system which achieves 3D high-accuracy localization using time and wrapped phase measurements of UWB signals. We expand the array aperture to improve the localization accuracy and address the phase wrapping problem using information fusion. Statistical characterization of ambiguous position estimates is encapsulated by soft positional information (SPI), which is approximated by Gaussian mixture model (GMM) to accelerate the filtering process. The parameters of GMM are estimated by an efficient parallel algorithm and two filtering methods are proposed to track the agent in different scenarios using approximate SPI. Finally, we implement the system on a low-cost platform and experimental results show that the proposed SAL system can achieve decimeter-level 3D localization accuracy in outdoor and indoor environments.

Precise range information is essential for high-precision network localization, where clock drifts will severely degrade the ranging accuracy. Two-way ranging methods are commonly adopted to mitigate those effects in localization networks but requiring a large amount of signal transmission to measure the distance between all pairs of nodes. This paper establishes a network localization framework, which fully mitigates clock drifts using only a minimum number of signal transmissions. The enabler is the proposed signal-multiplexing network ranging (SM-NR) method that minimizes communication overhead via signal multiplexing and eliminates clock drifts by exploiting the interconnections of timestamps. The proposed localization framework also allows some nodes to work in silent mode, of which the positions can be precisely determined without extra ranging signal transmissions. Simulation results show that the proposed algorithm can achieve high-precision localization in the presence of clock drifts with minimum signal overhead.

High-accuracy position awareness is essential to many applications, such as indoor navigation and autonomous vehicles. Wireless localization is a promising positioning service provider with the merits of extensive coverage and low cost but degrades in complex multipath propagation environments. This paper proposes a tensor-based algorithmic framework for localization in multipath environments by exploiting sparse spatio-temporal features of the received waveforms. Specifically, we construct low-rank tensors to characterize sparse spatio-temporal features, which can separate coherent multipath signals hinging on the uniqueness of tensor decomposition. Compared with related tensor-based works, our method does not rely on array configurations or signal structures, revealing its potential for broad use in multipath estimation. Position-related parameters are further extracted from tensor decomposition results, where a method based on the Chinese remainder theorem (CRT) is developed to retrieve distance information from the carrier part. Simulation results show that our method yields high-accuracy localization performance in complex multipath environments.

Coverage control is an essential problem in wireless camera sensor networks (WCSNs), and how to realize cooperative dynamic coverage control in WCSNs with anisotropic perception receives wide concern. In this paper, first we design a coverage metric integrating both the perception quality and the cover rate, in which the dynamic accumulation of coverage performance over time is also considered. To characterize the perception and the motion traits of the WCSNs, an anisotropic sensing model and the unicycle kinematic model are adopted. Then we propose a two-level cooperative dynamic coverage con-trol scheme for the WCSNs, which incorporates both the time-domain cooperation among time instants and the spatial-domain cooperation among agents. Compared with the traditional area-oriented methods, our scheme achieves target-oriented coverage based on the density function within the region. Numerical results verify the performance of our scheme in terms of the total and the perception cover rates.

Mobile multi-agent detection has enabled compre-hensive applications for intelligent sensing networks including Internet of Vehicles, Internet of Things and unmanned aerial vehicle formation. Regardless of the significant advantages of broad coverage and great flexibility, the implementation and popularization of sensing technologies are also limited by the inherent issues of position uncertainty, status update delay and sampling frequency. In this paper, we propose a detection performance evaluation scheme for distributed multi-agent detection in the presence of delayed update of agent positions. By deriving the spatial-temporal detection utility function across the network, we determine the influence mechanism of various non-ideal factors. Moreover, the universal lower bound of detection performance and the upper bounds for two scheduling policies are presented via asymptotic analysis on infinite time horizon. Numerical results further validate the superiority of delay-aware scheduling in mobile detection networks.

Location-based services are increasingly important in recent years, including Internet-of-Things, navigation, and rescue. Network localization is an effective technology to provide high-accuracy position awareness, where one of the most chal-lenging problems is multipath effects. In this paper, we propose a Block term (BT) decomposition-based approach to solving the multipath issue for multi-carrier systems. The method exploits both frequency diversity and temporal-domain sparsity, which applies to single-antenna systems and possesses the ability to distinguish more multipath components. Specifically, we first convert the temporal-domain observation into a low-rank tensor, and then BT decomposition is employed to separate mixed multipath components. Next, we estimate the delay from both baseband and carrier signals. When base stations are equipped with multiple antennas, our method will further extract the angle information. Simulation results show that the proposed method achieves high accuracy in multipath environments.

Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line-of-sight conditions. This paper proposes a deep generative model (DGM) for simultaneous range error mitigation and environment identification. In particular, we present a Bayesian model for the generative process of the received waveform composed by latent variables for both range-related features and environment semantics. The simultaneous range error mitigation and environment identification is interpreted as an inference problem based on the DGM, and implemented in a unique end-to-end learning scheme. Comprehensive experiments on a general Ultra-wideband dataset demonstrate the superior performance on range error mitigation, scalability to different environments, and novel capability on simultaneous environment identification.

Location-aware technologies are fast becoming a key instrument in civil applications. Ultra-wide bandwidth signals and their multipath components can provide precise channel information for radio frequency-based simultaneous localization and mapping (SLAM). In this paper, we provide a multipath-assisted single-anchor localization (MA-SAL) method to achieve robust localization of a user equipment as well as reflection points in indoor complex environments. In the proposed scheme, we develop a matching filter to determine the positions of the multipath components in the recorded channel impulse response and then estimate the delay and the angle-of-arrival of the signal on each propagation path. These estimates are fused with inertial measurement unit by an adaptive federated filter to infer the states of a user equipment and reflecting surfaces. Experimental results show that the proposed MA-SAL outperforms traditional single-anchor localization in terms of localization accuracy under indoor non-line-of-sight propagation conditions.

Intelligent multi-agent systems are prospective in various applications such as smart cities and smart factories, while the efficiency of scheduling problems of the agents often suffers from the kinematic constraints. In this paper, we establish the performance limit in the distributed scheduling of two agents with kinematic constraints. Specifically, the minimum achievable loss under full information is first analyzed, and then the robust loss is derived according to the available information. The robust analysis provides key insights on how to evaluate a situation, how to quantify the value of information for the efficient control, and how to characterize the benefit of cooperation between agents. Furthermore, a robust control policy is proposed based on the analysis, and simulation results are presented validating the efficiency of the policy. The robust analysis proposed in this paper provides a new perspective for handling the scheduling problems in intelligent multi-agent systems both theoretically and algorithmically.

The performance of visual 3D reconstruction is affected by camera position dithering, imaging noise and other environmental factors. In this paper, we adopt statistical analysis for camera dithering in visual 3D reconstruction and demonstrate the improvement of reconstruction accuracy brought by camera dithering under some conditions. In particular, we first investigate the influence of camera dithering and imaging noise on the reconstruction accuracy based on hybrid Cramér-Rao Lower Bound (CRLB) analysis. Then we focus on the binocular reconstruction and give closed-form expressions and geometric interpretations to clarify that camera dithering helps reduce reconstruction error. As an extension, the impacts of disparity and quantization error in binocular reconstruction are also analyzed. Numerical results validate our conclusions.

Typical methods for localisation in multipath environments focus on separating line-of-sight (LoS) from non-line-of-sight (NLoS) paths and only using LoS paths for localisation. A few works exploit NLoS paths but the methods are designed for some special settings. This paper presents a localisation method in which both LoS and NLoS paths are exploited for much more general settings. Its core is a convex optimisation formulation which handles multipath in a unified way, avoids error propagation between multiple stages, and guarantees a global convergence. In one of the case studies, single-antenna access points (APs) can locate a single-antenna mobile device (MD) even when all paths between them are NLoS, which according to the authors’ knowledge is the first time in the literature.

Text to speech (TTS) is widely used to synthesize personal voice for a target speaker, where a well-trained source TTS model is fine-tuned with few paired adaptation data (speech and its transcripts) on this target speaker. However, in many scenarios, only untranscribed speech data is available for adaptation, which brings challenges to the previous TTS adaptation pipelines (e.g., AdaSpeech). In this paper, we develop AdaSpeech 2, an adaptive TTS system that only leverages untranscribed speech data for adaptation. Specifically, we introduce a mel-spectrogram encoder to a well-trained TTS model to conduct speech reconstruction, and at the same time constrain the output sequence of the mel-spectrogram encoder to be close to that of the original phoneme encoder. In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder. AdaSpeech 2 has two advantages: 1) Pluggable: our system can be easily applied to existing trained TTS models without re-training. 2) Effective: our system achieves on-par voice quality with the transcribed TTS adaptation (e.g., AdaSpeech) with the same amount of untranscribed data, and achieves better voice quality than previous untranscribed adaptation methods1.

High-accuracy localization techniques have been widely used in various applications such as formation control and auto-driving where the relative position information is concerned. Existing works focus on efficient algorithm design to enhance the accuracy of relative localization while the spatial information coupling effect is not well revealed. In this paper, we develop a general approach inspired by the resistance distance theorem to characterize the spatial information coupling effect in relative localization. Then a geometric interpretation for the cooperation efficiency between two agents is presented via the random walk theory. Furthermore, we develop the asymptotic analysis for large lattice networks and prove that the relative localization error grows logarithmically with both network size and spatial distance. Finally, the theoretical analysis is validated by numerical results.

High-accuracy position awareness is essential to a variety of applications, including navigation, Internet-of-Things, and autonomous vehicles. Network localization is a promising positioning service provider with merits of wide coverage and low cost, while its precision degrades in complex multipath propagation environments due to mixed coherent signals. In this paper, we propose a tensor-based multipath estimation method for network localization, which fully explores the inherent structure in the measurements and the uniqueness of tensor factorization. Specifically, we represent the observation as a low-rank tensor and separate different propagation paths based on the sparsity in the spatio-delay domain via tensor factorization. Simulation results show that compared with conventional algorithms, the proposed tensor-based method effectively reduces the multipath effect and achieves the centimeter-level localization accuracy.

Accurate location information is essential for many emerging applications of the Internet of Things. Compared with absolute positions, relative positions of nodes are often more relevant in tasks such as formation control and autonomous driving. In this article, we propose a relative localization scheme for terminals aided by unmanned aerial vehicles. We first derive the performance limit of the relative position error as the constrained Cramér-Rao lower bound (CRLB), and proved that the equivalent Fisher information of a subnetwork retains all the information for its relative localization. We then develop a distributed localization algorithm based on local geometry transforming for the proposed scheme with low computation complexity. Moreover, an iterative descent algorithm is designed for joint power and spectrum allocation for relative localization. Numerical results show that the proposed localization algorithm significantly outperforms existing algorithms and the optimal allocation scheme can reduce the relative CRLB by about 50% compared with the uniform allocation scheme.

Locations of mobile agents are often requisite information for wireless applications such as sensor networks and Internet of Things (IoT). As the network size increases, verifying the localizability of all nodes in a network quickly becomes intractable. In this article, we turn to analyzing the unbounded localizability of infinite stochastic networks under sequential localization methods. Specifically, we prove the existence of the phase transition on the probability of localizing an unbounded subnetwork from a bounded initial anchor set in Poisson point process networks. The phase transition occurs when the node intensity of the network reaches a critical intensity, which is determined by the adopted sequential localization method. Furthermore, we develop a simulation method to obtain tight upper and lower bounds of the critical intensity for two-dimensional (2-D) networks with high confidence, and provide the numerical bounds under several typical sequential localization methods. We also show by simulation that the percentage of localizable nodes increases rapidly near the critical intensity, which provides guidelines for network design and deployment.

We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task. Current evaluation metrics all require annotated ground truth, thus will fail in the test environment and realistic circumstances prohibiting further optimization after training. By contrast, our metric reflects the internal characteristics of trajectory hypotheses and measures tracking performance without ground truth. We demonstrate that trajectories with different qualities exhibit different single or multiple peaks over feature distance distribution, inspiring us to design a simple yet effective method to assess the quality of trajectories using a two-class Gaussian mixture model. Experiments mainly on MOT16 Challenge data sets verify the effectiveness of our method in both correlating with existing metrics and enabling parameters self-optimization to achieve better performance. We believe that our conclusions and method are inspiring for future multi-object tracking in practice.

Collaborative formation is an important basis for multi-agent collaborative systems to complete advanced tasks. Previous studies show that the integrated framework of localization and control could achieve higher accuracy than the decoupled framework. We follow the integrated framework and design a feasible distributed formation algorithm based on relative measurements, which has near-optimal performance. Besides, we prove that if the measurement network topology is a connected regular graph, the proposed algorithm is asymptotically stable and an upper bound of the mean formation error is provided, which reveals how localization error and control error affect formation accuracy. Numerical results verify the performance of the proposed algorithm as well as the effects of the noise parameters and network topology on formation performance.

Intersection vehicle scheduling is a core issue influencing urban traffic fluency, and the development of the intelligent transportation system (ITS) provides novel potential for developing more appealing scheduling frameworks. Under most current approaches, the speed of vehicles passing the intersection is lower than the normal speed, which reduces the time-efficiency of the intersection. In this paper, we propose a distributed framework of long-horizon vehicle scheduling to eliminate potential conflicts among vehicles before they approach the intersection. Specifically, an algorithm for vehicles to select communication links and adjust their own positions based on the obtained delayed information is presented. The convergence guarantee of the algorithm is also provided for any finite vehicle flow. Furthermore, the influence of parameter selections on the algorithm performance is illustrated by numerical simulations, which also verify that the proposed algorithm better maintains traffic fluency than the heuristic approach. The proposed framework and algorithm provide a novel solution to improve traffic efficiency for intersections with enough preparation distances.

Visual 3D reconstruction is an essential technique in computer vision which restores the 3D model of the scene from multi-view images. In this paper, we propose a statistical framework for the active visual 3D reconstruction. We first derive a closed-form expression to characterize the dependence of the reconstruction performance on 3D point's position and camera setting parameters under the binocular camera setting. Then we derive several closed-form solutions and propose an efficient optimization algorithm to find the optimal camera configurations with the best reconstruction quality under various settings. Numerical results validate the performance of our methods in terms of both reconstruction accuracy and computational efficiency.

As a promising technology to improve the computation experience for mobile devices, mobile edge computing (MEC) is becoming an emerging paradigm to meet the tremendous increasing computation demands. In this paper, a mobile edge computing system consisting of multiple mobile devices with energy harvesting and an edge server is considered. Specifically, multiple devices decide the offloading ratio and local computation capacity, which are both in continuous values. Each device equips a task load queue and energy harvesting, which increases the system dynamics and leads to the time-dependence of the optimal offloading decision. In order to minimize the sum cost of the execution time and energy consumption in the long-term, we develop a continuous control based deep reinforcement learning algorithm for computation offloading. Utilizing the actor-critic learning approach, we propose a centralized learning policy for each device. By incorporating the states of other devices with centralized learning, the proposed method learns to coordinate among all devices. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves a better performance compared with discrete decision based deep reinforcement learning methods.

Large-scale antenna arrays offer considerable opportunities for high-accuracy network localization. However, such systems face severe resource challenges both from communication and computation, due to high-dimensional array signal processing. To reduce resource consumption, in this paper, we propose a lossless array signal dimension reduction scheme for multipath scenarios. The lossless scheme is determined by deriving and exploring the performance bound of localization systems in multipath environments. The effects of system parameters on the lossless scheme are also presented. Our results reveal that instead of directly processing high-dimensional array signals to seek high-accuracy localization, a comparable performance with significantly lower resource consumptions can be achieved by projecting signals into the low-dimensional subspace.

For the next generation of communication systems, low latency is an urging requirement to satisfy the increasing computation requires. In response, mobile edge computing (MEC) with energy harvesting (EH) is a promising technology to achieve sustained improvement of the computation experience. However, the frequently varied harvested energy, coupled with variable computing tasks and changing computation capacity of servers, results in the high dynamics of the computation offloading problem. In order to get satisfactory computation quality for such a high dynamic offloading problem, devices should learn to make multiple continuous and discrete actions when optimizing the system performance, such as latency, energy efficiency, etc. In this paper, we propose a continuous-discrete hybrid decision based deep reinforcement learning algorithm for dynamic computation offloading. Specifically, the actor outputs continuous actions (offloading ratio and local computation capacity) corresponding to every server. On the other hand, the critic outputs the discrete action (server selection) while also evaluates the performance of the actor for neural network updating. Simulation results validate the effectiveness of our proposed algorithm, which demonstrates superior generalization ability and achieves better performance compared with the discrete decision based deep reinforcement learning methods.

Mobile-edge computing (MEC) with energy harvesting (EH) is becoming an emerging paradigm to improve the computation experience for the Internet-of-Things (IoT) devices. For a multidevice multiserver MEC system, the frequently varied harvested energy, along with changeable computation task loads and time-varying computation capacities of servers, increase the system's dynamic. Therefore, each device should learn to make coordinated actions, such as the offloading ratio, local computation capacity, and server selection, to achieve a satisfactory computation quality. Thus, the MEC system with EH devices is highly dynamic and face two challenges: 1) continuous- discrete hybrid action spaces and 2) coordination among devices. To deal with such problem, we propose two deep reinforcement learning (DRL)-based algorithms: 1) hybrid-decision-based actor-critic learning (Hybrid-AC) and 2) multidevice hybrid-AC (MD-Hybrid-AC) for dynamic computation offloading. HybridAC solves the hybrid action space with an improvement of actor-critic architecture, where the actor outputs continuous actions (offloading ratio and local computation capacity) corresponding to every server, and the critic evaluates the continuous actions and outputs the discrete action of server selection. MDHybrid-AC adopts the framework of centralized training with decentralized execution. It learns coordinated decisions by constructing a centralized critic to output server selections, which considers the continuous action policies of all devices. Simulation results show that the proposed algorithms achieve a good balance between consumed time and energy, and have a significant performance improvement compared with baseline offloading policies.

The omnipresent signals of opportunity (SOOP) enable an effective way for passive target tracking using multiple agents. However, cooperative target tracking via non-cooperative SOOP is challenging since the positions of agents are not precisely known. In this paper, we determine the performance bounds of cooperative tracking using SOOP by multiple asynchronous agents equipped with antenna arrays. The Fisher information matrix of joint target and agent positions can be decomposed as the sum of the information from SOOP and self-localization networks, where the correlation of signals introduces an additional Fisher information component and the multipath effect is characterized by path-overlap coefficients. We demonstrate how the location information coupling between the target and agents affects localization accuracy and how the cooperation among agents improves tracking performance. Moreover, the angular information is shown to mitigate multipath and asynchronous effects by spatiotemporal separation and time-independent measurements, respectively. Then we propose a distributed hybrid belief propagation based algorithm for cooperative tracking and network synchronization via likelihood consensus. Finally, numerical results validate our theoretical analysis and the performance of the proposed algorithm.

Accurate wireless positioning of mobile agents is challenging in non-line-of-sight (NLOS) propagation environments due to unknown range or angle biases. In this paper, we develop a cooperative localization algorithm for mixed line-of-sight (LOS)/NLOS environments where the NLOS effect is mitigated by exploiting the geometric relationship of the range biases. In particular, we cast the localization problem as a detection-aided optimization program, in which all the distance measurements are initially treated as NLOS links with unknown nonnegative biases, followed by iterative agent position estimation and LOS identification. Moreover, the maximum-likelihood estimator for the agent positions and NLOS biases is relaxed into a semidefinite program where the geometric relationship of the biases is introduced as constraints. We also characterize the cooperation gain for LOS identification, and derive the constrained Cramér-Rao bound to show the localization accuracy improvement by the geometric constraints. Finally, numerical results validate the superior performance of the proposed algorithm compared with other competitive methods.

Accurate location information is indispensable for the emerging applications of Internet of Vehicles (IoV), such as automatic driving and formation control. In the real scenario, vision-based localization has demonstrated superior performance to other localization methods for its stability and flexibility. In this paper, a scheme of cooperative vision-based localization with communication constraints is proposed. Vehicles collect images of the environment and distance measurements between each other. Then vehicles transmit the coordinates of feature points and distances with constrained bits to the edge to estimate their positions. The Fisher information matrix (FIM) for absolute localization is first obtained, based on which we derive the relative squared position error bound (SPEB) through subspace projection. Furthermore, we formulate the corresponding bit allocation problem for relative localization. Finally, a variance-based gradient descent (V-GD) algorithm is developed by considering the influence of photographing, distance measurements and quantization noises. Compared with conventional bit allocation methods, numerical results demonstrate the localization performance gain of our proposed algorithm with higher computational efficiency.

Direct localization systems with large-scale antenna-arrays can greatly improve the localization accuracy by jointly processing all the observed signals. However, it incurs high communication overhead due to high-dimensional array signal transmission. In this paper, we propose a robust beamspace design technique in the presence of parameter uncertainty that can achieve high-accuracy positioning only with limited communication overhead. The beamspace design problem is formulated as a robust optimization in order to guarantee the worst-case performance in terms of the squared position error bound (SPEB). Since the problem is non-convex, we relax it to a convex programming and further prove that the solution of the relaxed problem converges to the optimal solution of the original problem. Simulation results validate the effectiveness and robustness of the proposed beamspace.

Cooperative detection in multi-agent networks has raised increasing concern for a variety of collaborative applications. Thorough research has been developed assuming exact knowledge about the space or space-time signature vector embedded at the received signal in fixed networks, but most of them fail to apply to a mobile scenario with inaccurate agent positions. In this paper, we propose a direct generalized likelihood ratio test (DGLRT) not only taking the position uncertainty of agents into consideration, but also obtaining the optimal steering vector by directly estimating target and agent positions instead of implementing testing under each possible intermediate parameter combination of range and angle-of-arrival (AOA). Furthermore, we conduct performance analysis on the proposed DGLRT and introduce the notation of effective signal-to-noise ratio (SNR) to measure its detection capability, from which the detection performance loss induced by position uncertainty is revealed. Comparison to the conventional detector demonstrates the superior detection performance of the DGLRT based on direct estimation of target and agent positions.

Target localization using signals of opportunity (SOOP) suffers performance degradation due to location uncertainties of mobile agents. In this paper, we investigate the performance limits of cooperative target localization in array networks using SOOP. The Fisher information matrix for target and agent positions is derived as a weighted sum of delay and angle information. The former is proportional to the effective bandwidth, and the latter is proportional to the product of the carrier frequency and geometric factor. In addition, we demonstrate how the cooperation among agents improves target localization performance, and how the information coupling between the target and agents affects localization accuracy. Then a general distance resolution limit based on the Fisher information analysis is derived as the metric for resolution performance. Finally, numerical results are provided to validate our theoretical analyses.

The position and time of mobile agents are essential information for many wireless network applications. In this paper, we propose a single-anchor localization and synchronization scheme of full-duplex (FD) agents in the line-of-sight scenario, where the anchor is equipped with an antenna array. The agents perform inter-node ranging using the FD radios, while the anchor only receives signals transmitted from the agents. We derive the Cramér-Rao lower bounds for the agent positions and clock offsets based on the frames received by the agents and anchor. In particular, the Fisher information matrix for the positions and clock offsets can be decomposed into the information from the measurements of the arrival angles and arrival times. Then, we design the channel estimation algorithm to obtain the arrival angles, arrival times, and signal-to-noise ratios, based on which we further develop the network localization and synchronization algorithms to obtain the agent positions and clock offsets. The simulation results illustrate the performance of our proposed scheme and algorithms.

Accurate indoor localization has attracted much research interest in the past decades. Utilizing existing WiFi infrastructures and inertial measurement units on smartphones is a promising method for indoor localization. In this paper, we propose a multi-grained grid-based filter to jointly estimate the location and the heading direction which can efficiently use map information for localization. Our method calculates the transition probability of location on fine-grained grids and only keeps location probabilities on coarse grids. The on/off model and the wall attenuation factor model are applied to generate observation likelihood from WiFi RSSI measurements. We implemented the algorithm on smartphones and it can run in real time. Experimental results have shown that the root mean squared error of the proposed method is 1.46m and the 90th percentile error is 2.18m.

Ultra-Wide Band (UWB) radio is capable of providing sufficient information for high accuracy localization. However, its actual performance is degraded due to the non-line-of-sight (NLOS) propagation. This paper introduces a probabilistic learning approach to mitigate the ranging error and yield uncertainties which correlate with the mitigation results. By combining variational inference with probabilistic neural networks, we propose a new probabilistic deep learning architecture, which can improve the accuracy significantly especially when the training data is limited. Results show that the proposed model can reduce the root mean square error (RMSE) of UWB ranging by 16%~56% compared with existing support vector machine approach in practical environment.

Wireless networks are growing in the value of application in many areas, in which accurate clock synchronization is required when tasks are performed in a collaborative fashion among nodes. Especially, cooperative synchronization techniques lead to significant performance improvement compared with traditional methods. However, the correlation among agents renders the performance analysis of cooperative network synchronization difficult. In this paper, we introduce the concept of information coupling intensity to the analysis of interaction between agents. Our approach enables us to derive closed-form asymptotic expressions under specific network topologies, and relate them to various network parameters.

High-accuracy formation is of great significance for multi- agent systems to perform complex tasks, and the accuracy of the formation is determined jointly by the network localization and formation control procedures. Existing studies commonly treat the two procedures separately and do not exploit an integrated design, leading to suboptimal formation performance. This paper establishes a general framework for high-accuracy multi-agent formation by integrated localization and control. In particular, we first propose a new metric called formation error to characterize the minimum squared distance between a real formation and a target one over arbitrary translation and rotation. Then we develop an integrated localization and control scheme to minimize the formation error. In the case study, we design the minimum mean formation error control algorithm along with a specific link selection strategy. Numerical results validate the performance gain of the integrated scheme over existing methods, and demonstrate effects of system parameters, which can serve as a guideline for practical system design.

Accurate position information is critical for the emerging applications of autonomous vehicles. Compared with absolute position information, relative positions are often more relevant for neighboring vehicles to perform joint tasks such as fleeting and overtaking. In this paper, we propose a relative localization scheme for ground vehicles aided by the unmanned aerial vehicles (UAVs). Each UAV makes ranging measurements with ground base stations and other UAVs in the round-trip time mode. The UAVs then broadcast their measurements to the ground vehicles, and each vehicle estimates its position based on the received data and the time difference of arrival (TDOA) of the signals from the UAVs. The Craḿer-Rao lower bound for the vehicles' absolute position errors is derived using the information inequality, based on which the relative position error bound is obtained through subspace projection. Finally, a two- stage localization algorithm for the vehicles is developed, which estimates the UAVs' positions followed by a weighted TDOA method. Numerical results show that the relative position errors between vehicles can reach the decimeter level even with meter- level absolute position errors.

The ability to localize a majority of nodes is important in many scenarios concerning large-scale stochastic networks. Classical percolation, i.e., the existence of infinite connected components in a stochastic network, has been analyzed in previous studies. However, a single connection is not enough for localization, which requires at least two connections to anchors. In this paper, we generalize classical percolation to localization problems. We find that for distributed localization, phase transition appears around a critical network density, i.e., an infinite localizable subnetwork exists with probability zero in the sub- critical case and with probability one in the super-critical case. Further, simulation methods are developed to estimate upper and lower bounds of critical densities, and precise estimates of densities in angle-of-arrival (AOA) and time-of- arrival (TOA) localization are performed using proposed methods.

Direct localization using antenna-array systems outperforms two-step localization algorithms by jointly processing all the measurements observed at base stations. However, it incurs high communication cost especially when the arrays have a large number of antennas. Local computing can offload such communication traffic by signal preprocessing and data compression at local platforms. In this paper, we propose beamspace design methods that can provide the best localization accuracy under communication constraints. We first derive the performance limits of beamspace direct localization and provide an optimal beamspace design with lossless signal compression.We then prove that two beams are sufficient for direct localization, regardless of the antenna numbers. Moreover, we propose a robust formulation for beamspace design in the presence of agent position uncertainty and quantify the performance gap to the ideal scenario. Simulation results validate that the proposed direct localization with low-dimensional beamspace signals can achieve near-optimal performance.

Source localisation withtime‐difference‐of‐arrival (TDOA) and frequency‐difference‐of‐arrival (FDOA)measurements is of great interest since it can provide the location information with highaccuracy.Although the maximumlikelihood (ML) estimator exhibits excellent asymptotic properties, the non‐linearity and non‐convexity of ML estimator requiremuch computation resources.In this study, source localisation with TDOA and FDOA measurementsis developed viaMonteCarlo importance sampling (IS).In particular, the optimalperformance can be guaranteed by constructing an optimalimportance function whosecovariance is equivalent to the inverse of Fisher information matrix.The derived variance of the proposed estimator showsgood consistency with the theoretical lowerbound. The improved performance of the proposed method is due to the optimal selection ofimportance function and it canconverge to the global optimum with a large number of samples. Although an initial estimate of source localisation information isrequired, the proposedmethod is robust to this a priori knowledge via IS. Moreover, the scenario ofconsidering sensor location uncertainties is analysed and the corresponding IS based solution is derived. Simulation results show that the proposed methods can achieve the Cramér–Rao lower bound at moderate level noises and is superior to several existing methods.

Reliable and accurate position information is of great importance for many mass-market and emerging applications. Network localization and navigation (NLN) is a promising paradigm to provide such information ubiquitously, where a network of nodes is used to aid in localizing its members. This paper explores various network operation strategies, which play an essential role in NLN as they determine the network lifetime and localization accuracy. Efficient network operation requires several functionalities, including node prioritization, node activation, and node deployment. The roles of these functionalities are described and different techniques for implementing respective functionalities via algorithmic modules are introduced. Some important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies are also introduced. Finally, numerical results are provided to demonstrate the localization performance improvement attributed to the optimized network operation strategies.

Network localization and navigation (NLN) is a promising paradigm, in which mobile nodes exploit spatiotemporal cooperation, to provide reliable location information for a diverse range of wireless applications. This paper presents a theoretical foundation of NLN, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. Key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation are then studied. We also develop a geometric interpretation to provide insights into the essence of NLN for network design. Finally, the paper highlights the connection between the theoretical foundation and algorithm development for NLN, guiding the design and operation of practical localization systems.

Network localization is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. To enable efficient network localization, we propose node prioritization strategies for allocating transmission resources among network nodes. This paper develops a computational geometry framework for determining the optimal node prioritization strategy. The framework consists of transforming each node prioritization strategy into a point in a Euclidian space and exploiting geometric properties of these points. Under this framework, we prove the sparsity property of the optimal node prioritization vector (NPV) and reduce the search space of the optimal NPV. Our approach yields exact optimal solutions rather than ε-approximate solutions for efficient network localization. Numerical results show that the proposed approach can significantly reduce the computational complexity of prioritization strategies and improve the accuracy of network localization.

Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information obtained by each agent is a complex compound due to the interaction among its neighbors. This information coupling may result in poor performance: algorithms that discard information coupling are often inaccurate, and algorithms that keep track of all the neighbors' interactions are often inefficient. In this paper, we develop a principled framework to characterize the information coupling present in network navigation. Specifically, we derive the equivalent Fisher information matrix for individual agents as the sum of effective information from each neighbor and the coupled information induced by the neighbors' interaction. We further characterize how coupled information decays with the network distance in representative case studies. The results of this paper can offer guidelines for the development of distributed techniques that adequately account for information coupling, and hence enable accurate and efficient network navigation.

The emerging technologies in 5G network not only bring revolutions in communications, but also promise new solutions in wireless localization. In this paper, we investigate how large-scale arrays impact the localization performance and derive the accuracy bound for wireless localization with multiple-input multiple-output (MIMO) systems. We then prove that when the number of array elements goes large, the multipath effects will vanish due to the asymptotic orthogonality of spatial channels in MIMO localization. The multipath mitigation can also be interpreted as the temporal and spatial separation supported by the signal bandwidth and the number of antennas, respectively. Finally, numerical results are given to validate our analysis.

Both localization and synchronization of mobile nodes are of fundamental importance for wireless networks. With the emergence of full-duplex (FD) communication technology, inter-node distances and clock offsets among a set of nodes can be simultaneously obtained through only two frames of communications, thus significantly improving the efficiency of node localization and synchronization. In this paper, we propose a localization and synchronization scheme using FD radios, and characterize its performance. Our study derives the Cramér-Rao lower bounds (CRLBs) for inter-node distances and clock offsets, the former of which can be translated into the estimation error bounds for localization. Comparison to conventional frequency division duplex (FDD) or time division duplex (TDD) demonstrates the high efficiency of localization and synchronization using FD radios. Our results reveal the potential of full-duplex technology beyond data communications in future wireless networks.

Resource management in the power and time-frequency domains is an important issue in distributed network localization. Since highly accurate ranging requires a large amount of time-frequency resources, cooperation among nodes without proper link selection may not be feasible. To address this issue, two resource management games are formulated, and Stackelberg equilibrium and link bargaining equilibrium are proposed as the solution concepts for efficient link selection and power allocation. Distributed algorithms are derived and analyzed using game theoretical approaches. It is demonstrated that the proposed strategies can achieve a lower mean squared error of position estimation with fewer ranging measurements.

Network navigation is a promising paradigm for providing location awareness in wireless environments, where nodes estimate their locations based on sensor measurements and prior knowledge. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements. The procedure of selecting node pairs at different times for inter-node measurements, referred to as network scheduling, affects the evolution of the localization errors. Thus, it is crucial to design efficient scheduling strategies for network navigation. This paper introduces situation-aware scheduling that exploits network states to select measurement pairs, and develops a framework to characterize the effects of scheduling strategies and of network settings on the error evolution. In particular, both sufficient and necessary conditions for the boundedness of the error evolution are provided. Furthermore, opportunistic and random situation-aware scheduling strategies are proposed, and bounds on the corresponding time-averaged network localization errors are derived. These strategies are shown to be optimal in terms of the error scaling with the number of agents. Finally, the reduction of the error scaling by increasing the number of simultaneous measurement pairs is quantified.

The fundamental limits of localization accuracy can be characterized by the information-theoretic bounds on position estimation. Tight bounds can be used not only as performance benchmarks for practical localization systems, but also criterion for assessing the effects of various sources of information (e.g., map awareness and ranging measurements) on localization performance, thus providing guidelines for the design and operation of localization systems. In this paper, we derive the Ziv-Zakai bound (ZZB) and Weiss-Weinstein bound (WWB) for map-aware cooperative localization, in which agents cooperate for position estimation with the aid of map information. The benefits from exploiting map awareness and agent cooperation are corroborated from both theoretical analysis and numerical examples, yielding important insights into how and when the map awareness can significantly improve the performance of cooperative localization.

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cramér-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: 1) a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and 2) a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

High-precision positioning techniques have a promising future in various applications. Recent work shows that inter-node cooperation can increase the positioning accuracy. In general, such cooperation consumes additional power, and current power application techniques are limited in synchronous networks. To study the power allocation in asynchronous networks, this paper proposes a power management game for cooperative localization, where each user allocates power that minimizes its square error positioning bound (SPEB) penalized by the power cost. Such game-theoretic power allocation policy not only considers how to allocate power over different cooperative links, but also manages the total power consumed for a better performance and power trade-off. We show that, in two-user networks, the power management game admits a unique cooperating Nash Equilibrium (NE) in high SNR region. In addition, as the power budget goes to infinity, the proposed game-theoretic power allocation policy can achieve SPEB arbitrarily close to that under exhaustive power allocation, but requires much less power.

Wireless networks are subject to security vulnerability due to the broadcasting nature of radio transmission. Information-theoretic approaches for secure communication propose to generate secret keys from a common source, such as reciprocal channels, available to the transmitter and receiver. However, such approaches assume the probability distribution of the sources, which may not be available in many realistic scenarios. In this paper, we establish an information-theoretic framework for secret-key generation (SKG) using noisy observations of unknown deterministic parameters (UDPs). Based on an axiomatic definition of UDPs, we derive a new metric called intrinsic information between the UDP and its observation, characterizing the rate of the secret key that can be generated from the observation. This metric is then applied to quantify the use of wideband channels in mobile networks for SKG. Our results provide a non-Bayesian perspective for SKG as well as its practical implications.

Network navigation is an emerging paradigm for enabling location-awareness in wireless networks. The limited wireless resource calls for scheduling algorithms to select node pairs for inter-node measurements. The design of efficient scheduling algorithms relies on an understanding of the underlying localization error evolution. This paper unifies the error evolution analysis in both the non-Bayesian and Bayesian cases. We first develop a unified recursive equation for the error evolution in the two cases. We then provide sufficient conditions for the boundedness of the error evolution. These conditions are more general than our previous results and can be used to derive error bounds for different scheduling algorithms. Furthermore, we show the the reduction of the error scaling with respect to the number of agents by increasing the multiplexing factor. These results provide insights for the efficient operation of wireless navigation networks.

Wireless network localization (WNL) is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. The localization performance of a node in WNL is determined by the allocation of transmit resources among its neighboring nodes. To achieve the best localization performance, we develop a computational geometry framework for optimal resource allocation in WNL. We first determine an affine map that transforms each resource allocation strategy into a point in 3-D Euclidian space. By exploiting geometric properties of these image points, we prove the sparsity property of the optimal resource allocation vector, i.e., the optimal localization performance can be achieved by allocating resources to only a small subset of neighboring nodes. Moreover, these geometric properties enable the reduction of the search space for optimal solutions, based on which we design efficient resource allocation strategies. Numerical results show that the proposed strategies can achieve significant improvements in both localization performance and computation efficiency. Our approach provides a new methodology for resource allocation in network localization, yielding exact optimal solutions rather than ϵ-approximate solutions.

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments via spatiotemporal cooperation. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the joint design of inference and control algorithms. In this paper, we develop efficient algorithms for network navigation with power control. In particular, we first determine the confidence region for location inference based on Fisher information analysis, and then design robust power control that minimizes the location inference errors of the agents within the confidence region. Both centralized and distributed algorithms are developed for energy-efficient network navigation. Simulation results show that the proposed algorithms significantly reduce the location inference errors compared to those without power control.

Device-to-device (D2D) communication in cellular networks is a promising concept that permits cooperation among mobile devices not only to increase data throughput but also to enhance localization services. In those networks, the allocation of transmitting power plays a critical role in determining network lifetime and localization accuracy. Meanwhile, it is a challenging task for implementation in cooperative D2D networks, since each device has only imperfect estimates of local network parameters in distributed settings. In this paper, we establish an optimization framework for robust power allocation in cooperative wireless network localization, and develop distributed power allocation strategies. In particular, we decompose the power allocation problem into infrastructure and cooperation phases, show the sparsity property of the optimal power allocation, and develop efficient power allocation strategies. Simulation results show that these strategies can achieve significant performance improvement in localization accuracy compared to the uniform strategies.

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the design of joint inference and control algorithms. In this paper, we develop efficient network navigation algorithms with optimized energy allocation. In particular, we first determine the confidence region for lzocation inference based on Fisher information analysis, and then design robust energy allocation strategies that minimize the position errors of the agents within the confidence region. Based on these strategies, both centralized and distributed energy-efficient network navigation algorithms are developed. Simulation results show that the proposed algorithms significantly reduce the position errors compared to the algorithms with uniform or non-robust power control.

Location-awareness is essential for many wireless network applications in both civil and military sectors. In this paper, we determine the localization accuracy of narrowband localization systems in which each mobile agent is equipped with an antenna array. Due to non-coherent estimators, the phases of the received signals can only be exploited for angle-of-arrival (AOA) estimation but not time-of-arrival (TOA). Based on such estimators, we derive the fundamental localization accuracy in terms of the squared position error bound (SPEB) in far-field harsh multipath environments. Moreover, we characterize the effects of the geometry of anchors and array antennas on the localization accuracy, yielding the criteria for optimal array design and network deployment. Our analysis exploits all the TOA and AOA information in the received waveform for localization using narrowband array-based systems, and the resulting SPEB serves as a fundamental limit for such systems.

Accurate position information enables numerous location-based applications. Wireless network localization is a promising localization technique that permits cooperation among mobile objects to enhance localization services. The allocation of transmitting power for such networks plays a critical role since it determines network lifetime, throughput, as well as the localization accuracy. In this paper, we establish an optimization framework for power allocation in cooperative localization networks. We first show that the optimal solution for the power allocation problem can be obtained by semi-definite programs (SDPs). For implementation in cooperative localization networks, we develop efficient and distributed power allocation strategies via relaxation of the original problem. In particular, we decompose the power allocation problem into infrastructure and cooperation parts. We transform the former into SDPs and derive upper bounds for the localization accuracy of the latter. These bounds enable us to develop efficient distributed strategies. Simulation results show that these strategies can achieve significant performance improvement compared to the unoptimized strategies in terms of localization accuracy.

Location-awareness plays a key role in various applications in future wireless networks. In GPS-challenged environments, location-awareness can be achieved via wireless navigation networks, which call for an efficient scheduling algorithm to optimize the navigation performance under communication constraints. In this paper, we develop a general framework for the design and analysis of scheduling algorithms for navigation networks with multiple measurement pairs per time slot. In particular, we provide sufficient and necessary conditions for the stability of the error evolution, and derive bounds on the time-averaged network localization errors (NLEs) for opportunistic and random scheduling. Furthermore, we show that the two scheduling algorithms are optimal in terms of the error scaling with respect to the number of agents, and we quantify the performance gain from measurement pair selections exploiting the network states. These results provide guidelines for designing efficient scheduling algorithms for network navigation.

Localization of wireless node is a key feature in many applications. Traditional localization has exploited the signal runtime between “agent” nodes that are to be localized and a set of “anchor” nodes with known position. Recently, cooperative localization that also uses runtime measurement between agent nodes has been shown to provide superior performance. This paper analyzes the optimum power and bandwidth allocation in such systems. We first formulate the general optimization problem and show that it is non-convex. We then develop an approximate algorithm based on Taylor expansion and iterative optimization of power and bandwidth separately to find an approximate solution; simulations show that results are close to the optimum solution (which is NP-hard). We also find that the importance of cooperative localization increases (and agents get assigned more resources) if the anchor deployment is bad in the sense that it provides high geometric dilution of precision and/or suffers from significant blockage between anchors and agents.

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms .

Wireless navigation networks enable location-awareness in GPS-challenged environments. For such networks, scheduling algorithms are needed to improve the navigation accuracy through measurement pair selections under limited communication resource. In this paper, we develop an analytical framework to determine the location error evolution for different scheduling algorithms and network settings. Under this framework, we provide sufficient conditions for the stability of the location error evolution, and we quantify the time-averaged network location errors (NLEs) for scheduling algorithms with and without exploiting the network states. Furthermore, we show the optimality of the proposed scheduling algorithms in terms of the error scaling with respect to the agent density. These results provide fundamental insights into the effects of scheduling algorithms and network settings on the location error evolution, leading to efficient scheduling algorithms for navigation networks.

High-accuracy localization is essential for many location-based applications. The position of an object can be obtained from range measurements based on wireless transmissions. Transmitting power allocation not only affects network lifetime and throughput, but also determines localization accuracy. The number of active transmitting nodes is also crucial since it is related to communication load and computation complexity. In this paper, we formulate the power optimization problem that provides the best localization accuracy under power constraints. We first prove the sparsity of the optimal power allocation, i.e., the optimal localization accuracy can be achieved by activating no more than (J) transmitting nodes in d-dimensional networks. Inspired by such sparsity, we derive the expressions of the optimal power allocation in 2-D networks. We also put forth a near-optimal algorithm for the power allocation problem with individual power constraints. Our results provide a theoretical basis for designing transmitting node selection and power allocation algorithms for network localization.

Power resource allocation is an important task for active and passive network localization, since it affects the localization accuracy in addition to the lifetime and throughput of the network. In this paper, we propose a robust power allocation formulation that guarantees the localization accuracy in the presence of parameter uncertainty for network localization. We first consider wireless network localization (WNL) as an example of active localization, and derive sequential upper and lower bounds for the worst-case localization accuracy as well as their convergence rates. Built on these bounds, we transform the robust formulation into a sequence of second-order cone programs (SOCPs) that yield asymptotically optimal solutions. We also develop an efficient near-optimal SOCP-based algorithm using a relaxation method. Then, we extend all the results to passive localization through the example of radar network localization (RNL). Finally, the simulation results validate the efficiency and robustness of the proposed algorithms.

The ability to exchange secret messages and protect against security attacks becomes increasingly important for providing information superiority and confidentiality in modern information systems. These systems require shared secret keys, which can be generated from common random sources with known distributions. However, the assumption on the distribution of the sources may not hold in many realistic scenarios. In this paper, we establish a mathematical framework for secret-key generation using common unknown deterministic sources (UDSs). In particular, we propose a new information measure called intrinsic information to characterize the achievable length of the secret key that can be generated from a UDS. As a case study, we consider a wideband propagation medium in mobile wireless networks as a UDS and derive its intrinsic information as a function of various network parameters. Our results provide a non-Bayesian perspective for secret-key generation as well as practical implications of this new perspective.

In wireless location-aware networks, mobile nodes (agents) typically obtain their positions using the range measurements to the nodes with known positions. Transmit power allocation not only affects network lifetime and throughput, but also determines localization accuracy. In this paper, we present an optimization framework for robust power allocation in network localization with imperfect knowledge of network parameters. In particular, we formulate power allocation problems to minimize localization errors for a given power budget and show that such formulations can be solved via conic programming. Moreover, we design a distributed power allocation algorithm that allows parallel computation among agents. The simulation results show that the proposed schemes significantly outperform uniform power allocation, and the robust schemes outperform their non-robust counterparts when the network parameters are subject to uncertainty .

High-accuracy localization is crucial for numerous location-based applications. In wireless networks, the position information of a node (agent) can be obtained from range measurements with respect to nodes with known positions (anchors). The transmission power allocation among anchors not only affects network lifetime and throughput, but also determines the localization accuracy. In this paper, we formulate the power optimization problem that provides the best location accuracy under a total power constraint. For a given set of anchors, the minimum number of active anchors required for optimal localization in 2-D network is proven to be either two or three, depending on the network parameters. We then derive the closed-form expression for the optimal power allocation in the case of small networks and extend the results to general cases. We also develop a near-optimal strategy that requires less computational complexity, incurring negligible average performance loss. Our results provide a theoretical basis for designing anchor selection and power allocation algorithms for localization.

The availability of reliable positional information is a key enabler for numerous emerging location-based applications. Network navigation via joint spatial and temporal cooperation can provide mobile nodes with high-accuracy and robust positional information. Meanwhile, this joint cooperation incurs intricate information acquisition due to the correlation in inferred nodes' position, referred to as information coupling. In this paper, we quantify the information coupling in four representative scenarios by Fisher information analysis. We show that the information obtained by each node is a sum of the contribution from its own spatio-temporal cooperation and information coupling due to the cooperation of its neighbors. Our results shed lights on the complex information acquisition in network navigation, and can serve as a design guideline for efficient network navigation algorithms.

Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we present an optimization framework for range-based localization to improve the power efficiency and localization accuracy. Our framework unifies the analysis for active and passive localization through the examples of wireless network localization (WNL) and multiple radar localization (MRL). In particular, we determine the functional properties of localization accuracy metric, and based on those properties we formulate the power allocation problem as conic programs. Moreover, we propose robust counterparts that retain the conic structures for power allocation in the presence of parameter uncertainty. Our simulation results validate the efficiency and robustness of the proposed methods.

In wireless localization systems, mobile nodes (agents) typically obtain their positions through ranging with respect to fixed infrastructure (anchors). Transmission power allocation not only affects network lifetime, throughput and interference, but also determines the localization accuracy. In this paper, we develop a robust anchor power allocation strategy to combat imperfect network topology parameters. We formulate the problem to minimize the squared position error bound (SPEB), which characterizes the fundamental limit of localization accuracy, and show that such formulation can be efficiently solved via semidefinite programming (SDP). The simulation results show that the proposed robust scheme significantly outperforms both non-robust scheme and uniform power allocation.

In cooperative localization networks, nodes estimate their locations through inter-node ranging, location information exchange, and information fusion. These operations cause packet collision, large communication overhead, and high computational complexity, which can be alleviated by proper scheduling techniques. In this paper, we design distributed scheduling algorithms for cooperative localization networks, which improve the efficiency of localization through neighbor selection and collision control. Then, we determine the evolution of each node's location error through cooperation and movement using Fisher information analysis. Furthermore, we analyze the convergence of the location error under the proposed scheduling algorithm. Simulation results show that the efficiency of localization is significantly improved by using the proposed scheduling algorithm.

Real-time and reliable location information of mobile nodes is a key enabler for many emerging wireless network applications. Such information can be obtained via network navigation, a new paradigm in which nodes exploit both spatial and temporal cooperation to infer their positions. In this paper, we establish a theoretical foundation for network navigation and determine the fundamental limits of navigation accuracy using equivalent Fisher information analysis. We then introduce the notion of carry-over information and provide a geometrical interpretation for the evolution of navigation information. Our framework unifies the navigation information obtained from spatial and temporal cooperation, leading to a deep understanding of information evolution and cooperation benefits in navigation networks.

Long term evolution (LTE) is considered to be a key technology for the next generation of cellular telecommunications. In LTE systems, each user equipment (UE) detects the surrounding cells by searching their identities (IDs) in the synchronization channels of the received waveform. Searching and tracking neighboring cells is important for cellular network management, such as handover and base station cooperation. In this paper, we establish a general framework for neighboring cell search (NCS) in LTE systems. In particular, we derive sufficient signal metrics (SSMs) for NCS under various channel conditions, and develop NCS algorithms based on the SSMs, which optimally combine multiple observations over space and/or time. Moreover, we develop a statistical model for NCS using probability analysis. The performance of NCS algorithms is characterized in terms of the number of detected cells and the cell detection probability, and simulation results validate the effectiveness of the proposed algorithms.

Real-time navigation capability is a key enabler for many emerging applications in wireless networks. Localization of moving nodes via network navigation gives rise to a new paradigm, where nodes exploit both temporal and spatial cooperation to determine their positions based on intra- and inter-node measurements. In this paper, we establish a theoretical foundation for network navigation to determine the fundamental limits of navigation accuracy. In particular, we derive the accuracy limits in terms of navigation information by equivalent Fisher information analysis. Our framework unifies the navigation information obtained from temporal and spatial cooperation, leading to a deep understanding of information exchange in the network and benefit of cooperation.

Many future wireless applications rely on the availability of position information for mobile wireless nodes (agents). Such information can be obtained through ranging and communication between agents and fixed infrastructure (anchors). Since the transmission power of the anchors affects network lifetime, throughput, and interference, in this paper we will investigate the problem of power allocation among anchors. We start with a formulation of minimizing the squared position error bound (SPEB), which characterizes the limits of location accuracy. However, the problem is difficult to solve due to its non-convexity. To address this issue, we leverage the insights obtained from the geometric interpretation of localization information and formulate the objective to maximum directional position error bound (DPEB). We first solve the problem for the single-agent case, and then extend the algorithm for the multiple-agent case. The simulation results show that the proposed scheme achieves close-to-optimal solution but with much lower computational complexity.

Traditional techniques for navigation such as the Kalman filter cannot capture the nonlinear and non-Gaussian models appearing in wireless localization systems deployed in harsh environments. Nonparametric filters as particle filters can cope with such models at the expense of a computational complexity beyond the reach of low-cost navigation devices. In this paper, we establish a general framework for parametric filters based on belief condensation (BC), which can express highly nonlinear and non-Gaussian system and measurement models. Our methodology exploits the specific structure of the problem and decomposes it in such a way that the linear and Gaussian part can be solved efficiently. The set of parameters for the posterior distribution is updated by an optimization process, referred to as BC. The simulation results show that the performance of the proposed parametric filter is close to that of the particle filter, but with a much lower complexity.

Long term evolution (LTE) is envisioned to be a key technology for the 4G wireless communication. In LTE systems, each user (UE) detects the surrounding base stations by searching the primary and secondary synchronization channel symbols in the received signal. Searching and tracking neighboring base stations is of great interest for UEs' handover and other applications such as base station cooperation. In this paper, we formulate the problem of neighboring cell search (NCS) for LTE systems and investigate this problem from both theoretical and practical perspectives. In particular, we first derive the sufficient signal metrics for NCS under various channel conditions and develop NCS algorithms based on these metrics. We also implement the algorithms on the simulator to validate its effectiveness.

Accurate positional information is essential for many applications in wireless networks. Time-of-arrival (TOA) and angle-of-arrival (AOA) are the two most commonly used signal metrics for localizing nodes with unknown positions. In this paper, we consider a wireless network in which each node is equipped with a wideband antenna array capable of performing both TOA and AOA measurements. Since both the position and orientation of the agent are of interest, we propose a localization framework that jointly estimates these two parameters. The notion of equivalent fisher information is applied to derive the squared error bounds for the position and orientation. Since our analysis starts from the received waveforms rather than directly from the signal metrics, these bounds characterize the fundamental limits of the position and orientation accuracy. Surprisingly, our result reveals that AOA measurements obtained by wideband antenna arrays do not further improve position accuracy beyond that provided by TOA measurements.

The availability of position information is of great importance in many commercial, public safety, and military applications. The coming years will see the emergence of location-aware networks with submeter accuracy, relying on accurate range measurements provided by wide bandwidth transmissions. In this two-part paper, we determine the fundamental limits of localization accuracy of wideband wireless networks in harsh multipath environments. We first develop a general framework to characterize the localization accuracy of a given node here and then extend our analysis to cooperative location-aware networks in Part II. In this paper, we characterize localization accuracy in terms of a performance measure called the squared position error bound (SPEB), and introduce the notion of equivalent Fisher information (EFI) to derive the SPEB in a succinct expression. This methodology provides insights into the essence of the localization problem by unifying localization information from individual anchors and that from a priori knowledge of the agent's position in a canonical form. Our analysis begins with the received waveforms themselves rather than utilizing only the signal metrics extracted from these waveforms, such as time-of-arrival and received signal strength. Hence, our framework exploits all the information inherent in the received waveforms, and the resulting SPEB serves as a fundamental limit of localization accuracy.

The availability of position information is of great importance in many commercial, governmental, and military applications. Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors). However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment. In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities. A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper. Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks. Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. We provide a geometrical interpretation of equivalent Fisher information (EFI) for cooperative networks. This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy. Our results yield important insights into how and when cooperation is beneficial.

The combination of wideband transmission and cooperative techniques enables high-precision location-awareness. Wideband transmission provides fine delay resolution and multipath resolvability, while cooperation among nodes can yield significant performance benefit in harsh or infrastructure-limited environments. In this paper, we propose to exploit the geometric relationship inherent in multipath propagation, i.e., multipath geometry, via cooperation among nodes for localization. We characterize the contribution of this multipath geometry in terms of the nodes' squared position error bound, which is the fundamental limit of localization accuracy. Analytical and numerical results validate the benefit of using multipath geometry in wideband cooperative localization.

Location-awareness is of pivotal importance in future wireless systems. In these networks, energy efficiency is a critical issue since it affects network lifetime, throughput, and interference. In this paper, we investigate optimization issues associated with anchor power allocation, anchor selection, and anchor deployment. In particular, we minimize the squared position error bound (SPEB), which characterizes the limits of localization accuracy. To gain insights into the localization problem, we use the eigen-analysis and propose a geometric interpretation of the localization information. This approach facilitates the derivation of optimal solutions and serves guidelines for the design of algorithms in energy efficient location-aware networks. Our numerical results show that significant improvement in localization performance can be achieved with optimal resource allocation.

Accurate positioning is an essential issue in location- aware wireless networks. Wideband transmission is inherent suitable for indoor localization due to its potential of providing accurate range measurements. In this paper, we will investigate the effect of path-overlap phenomena on the localization accuracy, since wideband signals traveling between nodes in the network experience multipath propagation. The notion of equivalent Fisher information (EFI) is proposed and applied to derive the squared position error bound (SPEB), a measure of the localization accuracy. EFI unifies the contributions from individual anchors in a canonical form, i.e., a sum of ranging information (RI), which characterizes the ability of a received waveform for localization. We show that the effect of path-overlap on the RI can be quantified by the path-overlap coefficient. Our numerical results present the behaviors of the path-overlap coefficient for different propagation channels and transmitted waveforms.

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed. The joint estimation of the forcing parameters and the system noise (volatility) in these dynamical systems is a crucial, but non-trivial task, especially when the system is nonlinear and multi-modal. We propose a variational treatment of diffusion processes, which allows us to estimate these parameters by simple gradient techniques and which is computationally less demanding than most MCMC approaches. Furthermore, our parameter inference scheme does not break down when the time step gets smaller, unlike most current approaches. Finally, we show how a cheap estimate of the posterior over the parameters can be constructed based on the variational free energy.

Determination of position accuracy for geolocation is a fundamental issue in wireless sensor networks. This paper derives the position error bound (PEB), a fundamental limit for localization accuracy, by using information inequality. In particular, the authors consider all multipath propagation parameters, and hence our bound is tighter than those of previous work. To alleviate computation complexity, the authors put forth the notion of equivalent Fisher information (EFI) to characterize the localization accuracy. This approach also unifies the contributions from line-of-sight (LOS), non-line-of-sight (NLOS), and a priori knowledge to the PEB in a consistent form. These results are applicable to ultra-wide bandwidth (UWB) systems as a specific case.

Determination of position accuracy for geolocation is a fundamental issue in wireless sensor networks. In a dense obstacle environment, anchors (or base stations) may not be able to provide sufficient localization information to agents because of radio blockage or limited range. In such cases, cooperation among agents (or mobile stations) can be very helpful. In this paper, we develop a model for cooperative localization based on time-of-arrival (TOA) ranging information and derive the position error bound (PEB) for agents in the network using information inequality. Equivalent Fisher information (EFI), which has been applied in the single agent localization case (Sen and Win, 2007), is employed to characterize the localization accuracy. From analysis, we also show that anchors and agents are essentially equivalent in our unified cooperative localization model.