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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.