Localizing users and mapping the environment using radio signals is a key task in emerging applications such as reliable low-latency communications, location-aware security, and safety-critical navigation. Recently introduced multipath-based simultaneous localization and mapping (SLAM) methods can jointly localize a mobile agent and map reflective surfaces in radio frequency (RF) environments. Most existing approaches assume that map features and their corresponding RF propagation paths are statistically independent, conditioned on the state of the mobile agent. This assumption neglects inherent dependencies that arise when a single reflective surface contributes to multiple propagation paths or when an agent communicates with multiple base stations. Existing approaches that aim to fuse information across propagation paths are further limited by their inability to perform ray tracing in RF environments with nonconvex geometries. In this paper, we propose a Bayesian multipath-based SLAM method for distributed multiple-input-multiple-output (MIMO) systems that addresses these limitations. In particular, we exploit amplitude statistics to establish adaptive, time-varying detection probabilities. Based on the resulting “soft” ray-tracing strategy, the proposed method can fuse information across propagation paths in RF environments with nonconvex geometries. A Bayesian estimation framework for the joint estimation of map features and agent state is developed by applying the message passing rules of the sum-product algorithm (SPA) to a factor graph representation of the proposed statistical model. We further introduce a new initialization procedure for reflective surfaces that enables the introduction of new surface states even when measurements arise solely from double-bounce paths. The proposed method is validated using both synthetic and real RF measurements obtained in challenging scenarios with nonconvex geometries and obstructed line-of-sight conditions. The results demonstrate that it provides accurate localization and mapping performance and approaches the posterior Cramér–Rao lower bound.

Passive acoustic monitoring (PAM) is a powerful and non-intrusive tool for studying marine mammals, particularly rare and deep-diving species such as beaked whales. However, the post-processing and manual analysis of large data sets is time-intensive. Multi-target tracking (MTT) methods have significant potential in automating this process and reducing human workload, but face challenges due to the potential absence or high directionality of beaked whale vocalizations. These two effects cause high variability in target detection probability, which can cause standard MTT trajectories to fragment. In this paper, we address this issue by introducing a multi-step estimation method that combines belief propagation-based MTT with track smoothing and stitching. Using real recordings of echolocation clicks from Ziphius cavirostris (Cuvier's beaked whales), we demonstrate that track stitching can generate continuous tracks in the presence of a significant number of consecutive missed detections.

Accurate navigation of autonomous underwater vehicles (AUVs) is a key task for data collection at sea with high resolution in time and space. Sidescan sonar (SSS), originally developed for imaging the seafloor, has high potential for establishing landmark-aided navigation or simultaneous localization and mapping (SLAM) capabilities on small-scale AUVs. A key task to establish these capabilities is to determine the height of the AUV from the seafloor, also referred to as “flight height.” This paper combines image processing techniques with probabilistic data association to detect and filter AUV flight height from SSS data. The proposed method first aims to detect the edge between the water column and the seabed using image processing techniques. (The pixel index of this edge is proportional to the flight height in meters.) Subsequently, a multisensor probabilistic data association filter (PDAF) fuses the resulting flight height detections computed from images provided by port and star-board SSS transducers to effectively mitigate missed detections and false positives. To facilitate deployments, we evaluate the performance of the proposed approach using real data collected by surface vehicles with SSS and demonstrate accurate flight height estimation from noisy SSS images.

This paper introduces new and practically relevant non-Gaussian priors for the Sparse Bayesian Learning (SBL) framework applied to the Multiple Measurement Vector (MMV) problem. We extend the Gaussian Scale Mixture (GSM) framework to model prior distributions for row vectors, exploring the use of shared and different hyperparameters across different measurements. We propose Expectation Maximization (EM) based algorithms to estimate the parameters of the prior density along with the hyperparameters. To promote sparsity more effectively in a non-Gaussian setting, we show the importance of incorporating learning of the parameters of the mixing density. Such an approach effectively utilizes the common support notion in the MMV problem and promotes sparsity without explicitly imposing a sparsity-promoting prior, indicating the methods’ robustness to model mismatches. Numerical simulations are provided to compare the proposed approaches with the existing SBL algorithm for the MMV problem.

We present a general Bernoulli Gaussian scale mixture based approach for modeling priors that can represent a large class of random signals. For inference, we introduce belief propagation (BP) to multi-snapshot signal recovery based on the minimum mean square error estimation criteria. Our method relies on intra-snapshot messages that update the signal vector for each snapshot and inter-snapshot messages that share probabilistic information related to the common sparsity structure across snapshots. Despite the very general model, our BP method can efficiently compute accurate approximations of marginal posterior PDFs. Preliminary numerical results illustrate the superior convergence rate and improved performance of the proposed method compared to approaches based on sparse Bayesian learning (SBL).

Particle flow (PFl) is an effective method for overcoming particle degeneracy, the main limitation of particle filtering. In PFl, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. Recently proposed stochastic PFl introduces a diffusion term in the differential equation that describes the trajectory of particles. This diffusion term reduces the stiffness of the differential equation and makes it possible to perform PFl with a lower number of numerical integration steps compared to traditional deterministic PFl. In this work, we introduce a general approach to perform importance sampling (IS) based on stochastic PFl. Our method makes it possible to evaluate a “flow-induced” proposal probability density function (PDF) after the parameters of the prior/predicted PDF represented by Gaussian mixture model (GMM) have been migrated by stochastic PFl. Compared to conventional stochastic PFl, the resulting processing step is asymptotically optimal. Within our method, it is possible to optimize the diffusion matrix that characterizes the diffusion term of the differential equation to improve the accuracycomputational complexity tradeoff. Our simulation results in a highly nonlinear 3-D source localization scenario showcase a reduced stiffness of the resulting stochastic PFl and an improved estimating accuracy compared to state-of-the-art deterministic and stochastic PFl.

In many multiobject tracking applications, including radar and sonar tracking, after prefiltering the received signal, measurement data is typically structured in cells. The cells, e.g., represent different range and bearing values. However, conventional multiobject tracking methods use so-called point measurements. Point measurements are provided by a preprocessing stage that applies a threshold or detector and breaks up the cell’s structure by converting cell indexes into, e.g., range and bearing measurements. We here propose a Bayesian multiobject tracking method that processes measurements that have been thresholded but are still cell-structured. We first derive a likelihood function that systematically incorporates an adjustable detection threshold which makes it possible to control the number of cell measurements. We then propose a Poisson Multi-Bernoulli (PMB) filter based on the likelihood function for cell measurements. Furthermore, we establish a link to the conventional point measurement model by deriving the likelihood function for point measurements with amplitude information (AM) and discuss the PMB filter that uses point measurements with AM. Our numerical results demonstrate the advantages of the proposed PMB filter for thresholded cell measurements compared to the conventional PMB filter for point measurements with and without AM.

We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOAs predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.

Multiobject tracking (MOT) is an important task in robotics, autonomous driving, and maritime surveillance. Traditional work on MOT is model-based and aims to establish algorithms in the framework of sequential Bayesian estimation. More recent methods are fully data-driven and rely on the training of neural networks. The two approaches have demonstrated advantages in certain scenarios. In particular, in problems where plenty of labeled data for the training of neural networks is available, data-driven MOT tends to have advantages compared to traditional methods. A natural thought is whether a general and efficient framework can integrate the two approaches. This paper advances a recently introduced hybrid model-based and data-driven method called neural-enhanced belief propagation (NEBP). Compared to existing work on NEBP for MOT, it introduces a novel neural architecture that can improve data association and new object initialization, two critical aspects of MOT. The proposed tracking method is leading the nuScenes LiDAR-only tracking challenge at the time of submission of this paper.

Localization using time-difference of arrival (TDOA) has myriad applications, e.g., in passive surveillance systems and marine mammal research. In this paper, we present a Bayesian estimation method that can localize an unknown number of static sources in 3-D based on TDOA measurements. The proposed localization algorithm based on particle flow (PFL) can overcome the challenges related to the highly nonlinear TDOA measurement model, the data association (DA) uncertainty, and the uncertainty in the number of sources to be localized. Different PFL strategies are compared within a unified belief propagation (BP) framework in a challenging multisensor source localization problem. In particular, we consider PFL-based approximation of beliefs based on one or multiple Gaussian kernels with parameters computed using deterministic and stochastic flow processes. Our numerical results demonstrate that the proposed method can correctly determine the number of sources and provide accurate location estimates. The stochastic flow demonstrates greater accuracy compared to the deterministic flow when using the same number of particles.

We propose a distributed multisensor joint integrated probabilistic data association (JIPDA) filter for multiobject tracking in decentralized sensor networks. Conventional Chernoff fusion of the posterior multiobject distributions of neighboring sensors presupposes a correct "hard" association of the objects tracked at the sensors. To avoid detrimental effects of an incorrect hard association, we develop a fusion method based on probabilistic ("soft") object association. Our numerical results demonstrate significant performance gains relative to the hard association approach.

Dynamic networks of physical nodes that require self-localization and synchronization are on the horizon. Existing infrastructure-based positioning and timing solutions are not fully sufficient for many applications. A peer-to-peer architecture known as cooperative localization and synchronization (CLAS) is a promising alternative when infrastructure is unavailable or insufficiently accurate. Efficient solutions for small, low-cost devices and platforms will be created by blending the functions of communication, localization, and synchronization into a single paradigm. Before CLAS can be widely adopted, we believe that it is necessary to develop localization and synchronization methods that do not impose additional constraints on the wireless communication system and are able to leverage a wireless broadcast transmission modality for enhanced scalability. We propose the first CLAS data fusion algorithm capable of operating over an unstructured communication network with no requirement for a custom pairwise communication protocol or global time anchors. The algorithm utilizes transmit/receive timestamp pairs from any received message, including a broadcast, and outputs an updated joint estimate of spatial and temporal state parameters. Both centralized and distributed versions of the algorithm are shown to be accurate and robust when used in conjunction with a commercially available ultra-wideband (UWB) radio. Results are presented for both simulated timestamps and data collected with a broadcast network of UWB radios.

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent’s position together with a time-varying number of multipath components (MPCs). The MPCs are represented by “delay biases” corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.

This paper introduces a statistical model and corresponding sequential Bayesian estimation method for terrain-based navigation using sidescan sonar (SSS) data. The presented approach relies on slant range measurements extracted from the received ping of a SSS. In particular, incorporating slant range measurements to landmarks for navigation constrains the location and altitude error of an autonomous platform in GPS-denied environments. The proposed navigation filter consists of a prediction step based on the unscented transform and an update step that relies on particle filtering. The SSS measurement model aims to capture the highly nonlinear nature of SSS data while maintaining reasonable computational requirements in the particle-based update step. For our numerical results, we assume a scenario with a surface vehicle that performs SSS and compass measurements. The simulated scenario is consistent with our current hardware platform. We also discuss how the proposed method can be extended to autonomous underwater vehicles (AUVs) in a straightforward way and why the combination of SSS sensor and compass is particularly suitable for small autonomous platforms.

Passive acoustics can provide a variety of capabilities with applications in oceanographic research and maritime situational awareness. In this paper, we develop a method for the navigation of autonomous underwater vehicles (AUVs) in shallow water. Our approach relies on passively recorded signals from acoustic sources of opportunities (SOOs). By making use of the waveguide invariant, a measurement of the range to the SOO is extracted from the spectrogram of a single hydrophone. Range extraction requires knowledge of the range rate, i.e., the radial velocity between the SOO and AUV, computed from the pressure fields at different time intervals. A particle-based navigation filter fuses the range measurements with the AUV’s internal velocity and heading measurements. As a result, the position error, which would otherwise increase over time, can be bounded. The ability to compute the range rate and range measurements from the pressure field measured using a single hydrophone is demonstrated on real data from the SWellEx-96 experiment. The capability of the developed navigation filter is shown based on synthetic data generated by the normal mode program KRAKEN.

Tracking targets based on measurements provided by radar, sonar, or lidar sensors is essential to obtain situational awareness in important applications, including autonomous navigation and applied ocean sciences. A key challenge in multitarget tracking is the unknown association between the available measurements and the targets to be tracked. In particular, robust data association for closely spaced targets requires advanced methods that explicitly model unresolved measurements. Due to limited sensor resolution, the sensor produces a single measurement for two or more actual targets. If not explicitly modeled in the multitarget tracking method, unresolved measurements lead to track losses and thus, to significant tracking errors. In this paper, we propose a scalable data association method for the tracking of multiple potentially unresolved targets. A loopy belief propagation method is presented that efficiently approximates the marginal association probabilities given a set of potentially unresolved measurements. This method scales quadratically in the number of targets and linearly in the number of measurements. Our numerical results demonstrate that the computed approximate marginal association probabilities are close in $L_{1}$ distance to the true marginal association probabilities, which can only be calculated for very small tracking scenarios.

Passive acoustic monitoring (PAM) is a nonintrusive approach to studying behaviors of vocalizing marine organisms underwater that otherwise would remain unexplored. In this paper, we propose a data processing chain that can detect and track multiple whales in 3-D from passively recorded underwater acoustic signals. In particular, time-difference-of-arrival (TDOA) measurements of echolocation clicks are extracted from a volumetric hydrophone array’s acoustic data by using a noise-whitening cross-correlation. For multi-target tracking, the TDOA measurements are then processed by a Bayesian inference engine consisting of two stages that is based on the sum-product algorithm (SPA). Particle flow is embedded in the SPA to make tracking computationally feasible in the considered nonlinear and high-dimensional scenario. The capability to track multiple whales without human intervention is demonstrated in scenarios with simulated and real data.

Tracking an unknown number of low-observable objects is notoriously challenging. This letter proposes a sequential Bayesian estimation method based on the track-before-detect (TBD) approach. In TBD, raw sensor measurements are directly used by the tracking algorithm without any preprocessing. Our proposed method is based on a new statistical model that introduces a new object hypothesis for each data cell of the raw sensor measurements. It allows objects to interact and contribute to more than one data cell. Based on the factor graph representing our statistical model, we derive the message passing equations of the proposed belief propagation (BP) method for TBD. Approximations are applied to certain BP messages to reduce computational complexity and improve scalability. In a simulation experiment, our proposed BP-based TBD method outperforms two other state-of-the-art TBD methods.

In the Industrial Internet of Things (i.e., IIoT), the standardization of open technologies and protocols has achieved seamless data exchange between machines and other physical systems from different manufacturers. At the MAC sublayer, the industry-standard protocols IEEE 802.15.4 Time Slot Channel Hopping (TSCH) and Deterministic and Synchronous Multi-channel Extension (DSME) show promising properties for high adaptability and dynamically changing traffic. However, performance comparison between these MAC protocols rarely has gone beyond a simulation phase. This work presents the results of such a comparison on physically deployed networks using the facilities of the FIT-IoTLab. The evaluation includes fully implementing an IIoT protocol stack based on MQTT in Contiki-NG. It comprises the integration of DSME as part of Contiki-NG’s software stack through OpenDSME, the only publicly available implementation of DSME. Results show that both protocols suit IIoT applications, particularly for data collection. The comparison between TSCH and DSME also includes an evaluation of distributed schedulers for both MAC modes and one autonomous scheduler for TSCH within a UDP protocol stack.

We present a sparsity-promoting method for the detection and estimation of the directions of arrival (DOAs) of source signals. The proposed method is based on the recently introduced variational Bayesian line spectral estimation (VALSE) approach, which is gridless. However, the performance of VALSE is sensitive to an initial guess of the measurement noise variance and potential DOAs. Thus, we propose a sparse Bayesian learning-aided initialization. Simulation results show that this learning-aided VALSE outperforms state-of-the-art DOA estimation methods as well as the conventional VALSE. We also evaluate the proposed method using acoustic data from an ocean acoustics experiment.

Cooperative localization is an enabling technology for the IoT that will introduce innovative services for modern convenience and public safety. Particle-based belief propagation (BP) is a state-of-the-art method for cooperative localization. However, in large and dense cooperative localization networks, particle-based BP suffers from particle degeneracy. In this paper, we propose a new method that combines particle-based BP and particle flow (PF) and can avoid this detrimental effect. To perform operations on the graph effectively, particles are moved towards regions of high likelihood based on the solution of a partial differential equation. We show that the proposed PF-BP algorithm can significantly outperform conventional particle-based BP in accuracy and runtime.

Location-aware networks enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. Network localization and navigation (NLN) is a recently proposed paradigm for accurate ubiquitous localization without the need for extensive infrastructure. In NLN, devices form an interconnected network for the purpose of cooperatively localizing one another. This paper introduces Peregrine, a system that combines real-time distributed NLN algorithms with ultra-wideband (UWB) sensing and communication. The Peregrine software integrates three NLN algorithms to jointly perform 3-D localization and network operation in a technology agnostic manner, leveraging both spatial and temporal cooperation. Peregrine hardware is composed of compact low-cost devices that comprise a microprocessor and a UWB radio. The contribution of each algorithmic component is characterized through indoor network experimentation. Results show that Peregrine is robust, scalable, and capable of sub-meter accuracy in challenging wireless environments.

Many MAC protocols for the Industrial Internet of Things, such as IEEE 802.15.4 and its extensions, require contention-based channel access for management traffic, e.g., for slot (de)allocations and broadcasts. In many cases, subtle but hidden patterns characterize this secondary traffic, but present contention-based protocols are unaware of these patterns and therefore cannot exploit them. Especially in dense networks, these protocols often do not provide sufficient throughput and reliability for primary traffic, i.e., they cannot allocate transmission slots in time. In this paper, we propose QMA, a contention-based multiple access scheme based on Q-learning. It dynamically adjusts transmission times to avoid collisions by learning patterns in contention-based traffic. We show that QMA solves the hidden node problem without the overhead for RTS/CTS messages and, for example, increases throughput from 10 packets/s to 50 packets/s in a hidden three-node scenario without sacrificing reliability. Additionally, QMA's scalability is evaluated in a realistic scenario for slot (de)allocation in IEEE 802.15.4 DSME, where it achieves up to twice more slot (de)allocations per second.

Estimating position and orientation change of a mobile platform from two consecutive point clouds provided by a high-resolution sensor is a key problem in autonomous navigation. In particular, scan matching algorithms aim to find the translation and rotation of the platform such that the two point clouds coincide. The association of measurements in point cloud one with measurements in point cloud two is a problem inherent to scan matching. Existing methods perform non-probabilistic data association, i.e., they assume a single association hypothesis. This leads to overconfident pose estimates and reduced estimation accuracy in ambiguous environments. Our probabilistic scan matching approach addresses this issue by considering all association hypotheses with their respective likelihoods. We formulate a holistic Bayesian estimation problem for both data association and pose inference and present the corresponding joint factor graph. Near-optimum maximum a posteriori (MAP) estimates of the sensor pose are computed by performing iterative message passing on the factor graph. Our numerical study shows performance improvements compared to non-probabilistic scan matching methods that are based on the normal distributions transform (NDT) and implicit moving least squares (IMLS).

We present a Bayesian method for sequential direction finding based on variational line spectral estimation (VALSE). The proposed method promotes sparse solutions by means of a Bernoulli-Gaussian amplitude model, is grid-less, and provides marginal posterior distributions from which DOA estimates and their uncertainties can be extracted. Simulation results demonstrate performance improvements in the considered scenario. We also evaluate the proposed method using acoustic data from an underwater source localization experiment.

This paper presents a factor graph formulation and sum-product algorithm (SPA) for scalable detection and tracking of extended objects that generate multiple measurements. The proposed method dynamically introduces newly detected objects into the state space and efficiently performs probabilistic multiple-measurement to object associations. We demonstrate performance advantages of our method numerically in a challenging tracking scenario with multiple closely-spaced extended objects.

Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements provided by one or multiple sensors. Additional information, such as imperfect estimates of target classes provided by a classifier, can facilitate the target-measurement association and thus improve MTT performance. In this letter, we describe how a recently proposed MTT framework based on the sum-product algorithm can be extended to efficiently exploit class information. The effectiveness of the proposed approach is demonstrated by simulation results.

The Deterministic and Synchronous Multi-Channel Extension (DSME) protocol is a recent amendment to the IEEE 802.15.4 standard. It combines contention-based and time-division medium access, offers channel diversity, and is aimed to support IIoT applications with stringent requirements in terms of timeliness and reliability. In this paper, we show how to configure DSME for a given data collection task. This includes the definition of the slot and frame length and the slot and channel schedule. We formulate different scheduling strategies as linear programs minimizing latency and energy. We verify our results through theoretical analysis and simulations and compare them with state-of-the-art scheduling algorithms. The results indicate a reduced delay of up to 80% for deep networks while also increasing reliability. Additionally, the proposed scheduling strategies significantly reduce the required buffer size.

The sum-product algorithm (SPA) was recently shown to provide a scalable methodology for multitarget tracking (MTT) using multiple sensors. Here, we focus on another advantage of the SPA frame-work, namely, its capacity for Bayesian fusion of heterogeneous data sources and auxiliary information. We develop extensions of the SPA-based multisensor MTT algorithm that integrate data from an auxiliary surveillance system and geographic information about standard target routes. The effectiveness of our approach is demonstrated for a simulated scenario and for a real maritime scenario.

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

Location aware networks will introduce new applications and services for modern convenience, the military, and public safety. In this paper, we introduce a Bayesian method for network localization and navigation in the presence of measurement-origin uncertainty (MOU). In the envisioned cooperative scenario, the agents in a dynamic network aim to better localize themselves by performing pairwise observations with other agents in their environment and sharing their location information. Since pairwise observations suffer from MOU, a data association problem has to be solved before an agent can update its location information. In our approach, joint inference is performed through a factor graph formulation of the entire, network-wide estimation problem. Performing the loopy sum-product algorithm on the derived factor graph results in a distributed and scalable inference algorithm. Simulation results demonstrate that cooperation among agents can significantly improve the localization accuracy even in the presence of MOU.

The Bernoulli filter (BF) is a Bayes-optimal method for target tracking when the target can be present or absent in unknown time intervals and the measurements are affected by clutter and missed detections. We propose a distributed particle-based multisensor BF algorithm that approximates the centralized multisensor BF for arbitrary nonlinear and non-Gaussian system models. Our distributed algorithm uses a new extension of the likelihood consensus (LC) scheme that accounts for both target presence and absence and includes an adaptive pruning of the LC expansion coefficients. Simulation results for a heterogeneous sensor network with significant noise and clutter show that the performance of our algorithm is close to that of the centralized multisensor BF.

The Internet of Things (IoT) will seamlessly integrate a large number of densely deployed heterogeneous devices and will enable new location-aware services. However, fine-grained localization of IoT devices is challenging as their computation and communication resources are typically limited and different devices may have different qualities of internal clocks and different mobility patterns. To address these challenges, we propose a cooperative, scalable, and time-recursive algorithm for network localization and synchronization (NLS). Our algorithm is based on time measurements and supports heterogeneous devices with limited computation and communication resources, time-varying clock and location parameters, arbitrary state-evolution models, and time-varying network connectivity. These attributes make the proposed algorithm attractive for IoT-related applications. The algorithm is furthermore able to incorporate measurements from additional sensors for positioning, navigation, and timing such as receivers for global navigation satellite systems. Based on a factor graph representation of the underlying spatiotemporal Bayesian sequential estimation problem, the algorithm uses belief propagation (BP) for an efficient marginalization of the joint posterior distribution. To account for the nonlinear measurement model and nonlinear state-evolution models while keeping the communication and computation requirements low, we develop an efficient second-order implementation of the BP rules by means of the recently introduced sigma point belief propagation technique. Simulation results demonstrate the high synchronization and localization accuracy as well as the low computational complexity of the proposed algorithm. In particular, in sufficiently dense networks, the proposed algorithm outperforms the state-of-the-art BP-based algorithm for NLS in terms of both estimation accuracy and computational complexity.

Situation-aware technologies enabled by multitarget tracking will lead to new services and applications in fields such as autonomous driving, indoor localization, robotic networks, and crowd counting. In this tutorial paper, we advocate a recently proposed paradigm for scalable multitarget tracking that is based on message passing or, more concretely, the loopy sum-product algorithm. This approach has advantages regarding estimation accuracy, computational complexity, and implementation flexibility. Most importantly, it provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitarget tracking. This fact makes it attractive for emerging applications requiring real-time operation on resource-limited devices. In addition, the message passing approach is intuitively appealing and suited to nonlinear and non-Gaussian models. We present message-passing-based multitarget tracking methods for single-sensor and multiple-sensor scenarios, and for a known and unknown number of targets. The presented methods can cope with clutter, missed detections, and an unknown association between targets and measurements. We also discuss the integration of message-passing-based probabilistic data association into existing multitarget tracking methods. The superior performance, low complexity, and attractive scaling properties of the presented methods are verified numerically. In addition to simulated data, we use measured data captured by two radar stations with overlapping fields-of-view observing a large number of targets simultaneously.

This paper addresses the problem of localizing an unknown number of static sources emitting unknown signals from time-difference of arrival (TDOA) measurements. Based on the framework of random finite sets and finite set statistics, we formulate the Bayesian estimation problem and develop a particle-based localization algorithm that overcomes the challenges related to the highly non-linear TDOA measurement model, the data associations uncertainty, and the uncertainty in the number of sources to be localized. Simulation results confirm that the number of sources can be determined correctly and accurate location estimates can be obtained when the number of false alarms is low and the probability of detection is high.

We propose a method for detecting and estimating multiple objects from multiple noisy images with partly overlapping observation areas. The goal is to detect the objects that are “locally” present in the individual observation areas and to estimate their states. Our method is based on a new closed-form expression of the marginal posterior probability hypothesis density (PHD) and admits a distributed implementation. Simulation results demonstrate performance gains over correlation-based and PHD-based methods that do not take advantage of the overlapping observation areas.

We propose a hybrid message passing method for distributed cooperative localization and tracking of mobile agents. Belief propagation and mean field message passing are employed for, respectively, the motion-related and measurement-related parts of the factor graph. Using a Gaussian belief approximation, only three real values per message passing iteration have to be broadcast to neighboring agents. Despite these very low communication requirements, the estimation accuracy can be comparable to that of particle-based belief propagation.

We propose a Bayesian method for cooperative localization and control in mobile agent networks. Distributed, cooperative self-localization of each agent is supported by an information-seeking control of the movement of the agents. For cooperative localization, the SPAWN message passing scheme is used. Cooperative control is achieved by maximizing the negative joint posterior entropy of the agent states via a gradient ascent. The localization part of our method provides the control part with sample-based probabilistic information. Simulation results demonstrate intelligent behavior of the agents and excellent localization accuracy.

We introduce a distributed cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.

Most distributed statistical signal processing methods assume conditionally uncorrelated sensor measurements although this assumption is often not satisfied. Here, we propose a distributed algorithm for decorrelating the sensor measurements in a wireless sensor network. The algorithm employs a matrix-valued Chebyshev approximation to achieve an approximate decorrelation using only local computations and communication between neighboring sensors. We apply the algorithm to consensus-based distributed particle filtering in a target tracking problem with correlated measurement noises. Simulations show that the decorrelation yields a substantial accuracy improvement while causing only a small communication overhead.

The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.

The recently introduced framework of cooperative simultaneous localization and tracking (CoSLAT) combines Bayesian cooperative agent self-localization with distributed target tracking. The original CoSLAT algorithm suffers from high computation and communication costs because it uses a particle-based message representation. Here, we propose an advanced hybrid particle-based and parametric message passing algorithm for CoSLAT in which both costs are significantly reduced. Simulation results show that the localization/tracking performance is not affected.