Multi-target tracking (MTT) in real-world environments often faces the challenge of outlier measurements, which severely degrades the performance of standard MTT algorithms. This paper integrates the maximum correntropy criterion (MCC) into the Gaussian mixture probability hypothesis density (GM-PHD) filter, an implicit data association and a highly efficient MTT algorithm. The MCC provides a localized similarity measure that is inherently resilient to impulsive outliers. We embed an iterative fixed-point measurement update for the GM-PHD filter, and an adaptive kernel size design strategy is also devised. The proposed MCC-GM-PHD filter effectively suppresses the influence of large measurement residuals, while maintaining a closed-form Gaussian mixture representation. The performance of the proposed MCC-GM-PHD filter is verified via simulations.

Tensors can efficiently represent high-dimensional data, simplify the modeling and computation of complex systems and enhance the performance and flexibility of algorithms in tasks such as multi-sensor fusion and nonlinear system estimation. Meanwhile, the unscented Kalman filter (UKF) directly handles nonlinear systems through the unscented transformation, avoiding linearization errors. This ensures estimation accuracy and numerical stability, making it suitable for highly nonlinear scenarios. Based on the Bayesian filtering principle, this article derives the UKF for recursively estimate tensors based on the streaming tensor measurements. The proposed algorithm leverages the tensor Kronecker product for deriving the covariance of tensor distribution based on the sigma points, which allow for accurately propagating the first two moments of tensor posterior. Simulation results show that the proposed tensor UKF (TUKF) in this letter outperforms the state-of-art algorithm, thus verifies the effectiveness of TUKF.

This paper considers the estimation of multiple 2D directions-of-arrival (DOAs) of sources, wherein both the number of sources and the DOA of a specific source are time-varying. Our work leverages dynamic models of 2D-DOAs in the spatial domain to predict the 2D-DOAs, and the likelihood function for a square array is formulated. Such modeling allows us to exploit the Bayesian estimation framework to recursively compute the posterior of multiple 2D-DOAs. In order to reduce computational complexity and ensure the conjugacy of prior and posterior PDFs, variational Bayesian inference (VBI) is employed further. Simulation results show that our approach outperforms the conventional 2D-MUSIC method, particularly under low signal-to-noise ratio (SNR) conditions, thus verifying the effectiveness of the proposed algorithm.

Allocating the weight to each local density is essential in generalized covariance intersection (GCI) fusion. However, such a problem has not been fully addressed in the existing literature and still remains an open issue. In this paper, we propose a deep learning enhanced framework that dynamically optimizes GCI fusion weights by leveraging sensor node dependent local variables, resulting in the GCINet for fusion of probability density functions (PDFs). The key innovation lies in the employment of contextual based variables (e.g., measurement noise) as input to a neural network, which is trained by minimizing a suitably defined cost function. The proposed approach eliminates the need for manual weight tuning and overcomes the limitations of traditional optimization-based methods reliant on, e.g., Shannon entropy or Chernoff information. Application of proposed GCINet to distributed extended object tracking (EOT) application is discussed. Simulation results show that the proposed GCINet achieves superior accuracy compared to GCI fusion under equal as well as heuristically designed fusion weights.

This paper presents an approach for 2-dimensional extended object tracking (EOT). The extended object (EO) is represented as a superellipse characterized by kinematic and shape states, with the latter uniquely specified in terms of four parameters. An approximated measurement model is proposed accounting for the fact that the measurements can be generated from any position inside or on the contour of the EO. Then, EOT is performed by iteratively estimating the kinematic state via a Kalman filter, while the posterior of the shape state is represented and propagated in particle filter form due to the strong nonlinearity of the resulting shape measurement model. Simulation results are provided to assess the performance of the proposed method.

This paper discusses the problem of streaming coupled tensor factorization based on sensor networks, where each sensor observes only some features of the targets, and the measurements from sensors are provided in a streaming tensor fashion. Moreover, the observed features of different sensors might overlap (i.e., coupled tensor), and there is no central processing unit to collect all sensor data. Then, in our work, the canonical polyadic (CP) decomposition is exploited to perform local tensor decomposition based on the measurements of each sensor, and average consensus (AC) for diffusing information throughout the network. The proposed method is verified via simulations.

A key feature of extended target (ET) is that it can produce multiple measurements, thus providing detailed information such as size, orientation. However, due to the measurement number decrease, the ET tends to be a point target (PT) when it becomes far from the sensor, leading to disability of estimating the extensions. In this paper we consider the problem of distributed ETs tracking based on multiple sensors. In such a case the measurements decrease of an ET can be compensated by other sensors, so that the tracking performance can be maintained. In the proposed algorithm the targets are modeled by Poisson multi-Bernoulli (PMB) random finite set (RFS) and the state of each target consists of two parts representing the possibility of being ET and PT, respectively. In the local filtering state, interaction between ET and PT states is considered in the prediction step for target identity change between ET and PT, based on which the local PMB filter is achieved for seamlessly tracking ETs and PTs. In the fusion part a generalized covariance intersection (GCI) based criterion is proposed to fuse the posteriors of each sensor. The performance of proposed algorithm is verified via simulations.

In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each individual node and data fusion among multiple nodes. For local filtering, the LMB filter is extended to JDTC by augmenting the target state to incorporate class and mode information. Further, the well-known generalized covariance intersection and recently developed minimum information loss fusion paradigms are exploited for data fusion among sensors. The effectiveness of the resulting algorithm, called D-JDTC-LMB, is assessed via simulation experiments.

Passive location and tracking of radio emitters is of great research value in civilian and defense applications. Among the existing methods, localization based on received signal strength indicator (RSSI) has been widely used due to its advantages in terms of low cost and easy implementation. However, most RSSI-based localization methods rely on the assumption that the emitter has been detected. Moreover, the emitter signal is supposed to propagate with the simplified path-loss model in which the shadow effects caused by obstacles are not considered. As a result, there are still gaps between the aforementioned methods and practical applications. In this paper, we consider the combined path-loss and shadowing model, which has been empirically confirmed in both outdoor and indoor radio propagation environments. Joint detection and tracking of an emitter is proposed by modeling the state of the emitter as Bernoulli random finite set, characterized by an existence probability and a spatial probability density function. Compared to existing studies, this paper works upon more practically appealing signal propagation model, and achieves better performance in real-time emitter detection and tracking. Moreover, the proposed method also provides explicit estimates of the unknown shadowing-related parameters, which can be adopted in further applications such as spectrum cartography and radio map construction. The feasibility of the proposed method is assessed via simulation experiments.

Target state estimation with Doppler-only sensors has attracted a lot of attention due to its wide potential applications in target localization and tracking. While existing Doppler-only tracking methods rely on the assumption that Doppler sensors have been correctly registered, in many practical cases there can be significant registration errors which imply measurement biases and thus performance degradation in target state estimation. Motivated by this issue, the present paper addresses the problem of jointly estimating target state and sensor biases based on Doppler-only measurements. The proposed method consists of two phases, i.e., (1) raw estimation of the target state without considering sensor biases, followed by (2) a bias compensation step that relies on linearization of the measurement function and joint estimation of target state-sensor biases via a least square method. The Cramer-Rao lower bound (CRLB) in estimating sensor biases is evaluated and the performance of the proposed method is also assessed via simulations.

This article addressessimultaneous localization and mapping(SLAM) viaprobability hypothesis density(PHD) filtering. The resulting approach, named PHD-SLAM, has demonstrated its effectiveness, especially when measurements provided by the sensors onboard the vehicle are highly contaminated by missdetections and clutter. However, since theproposal distribution(PD) of standard PHD-SLAM does not take into account most recently received measurements, a huge amount of particles are typically needed in order to achieve satisfactory performance. In this article, a new PD, which aims to approximate the vehicle pose posterior, is proposed for PHD-SLAM. The resulting algorithm, named PHD-SLAM 2.0, allows for drastically reducing the number of particles, and hence, the computational burden, while preserving the SLAM performance. The computational complexity of PHD-SLAM 2.0 is analyzed, and its performance is assessed via both simulated and real-data experiments.

A B-Spline implementation of the multi-target multi-Bernoulli (MeMBer) filter for nonlinear Gaussian/non-Gaussian models is proposed. Specifically, the spatial PDF (SPDF) of each Bernoulli component in the MeMBer density is represented by a B-Spline curve, which is characterized by the spline knots and control points. The spline knots and control points are then propagated via prediction and update steps of the MeMBer filter. Besides, a revised fitting algorithm is proposed so as to improve the implementation efficiency. The effectiveness of the proposed method is assessed via simulation experiments.

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates.

The linear opinion pool (LinOP) provides a potential solution to the problem of information fusion. However, the LinOP cannot be directly applied to multi-object fusion since the resulting fused multi-object density, in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in Bayesian multi-object filtering. In this letter, by showing that the LinOP is actually the one that leads to minimum information loss (MIL), we propose to find the fused multi-object density that has the same form as the local ones and, at the same time, leads to MIL. The performance of MIL fusion is then compared with the one of the well-known generalized covariance intersection (GCI) fusion via simulations.

This article addresses fully distributed multirobot (multivehicle) simultaneous localization and mapping (SLAM). More specifically, a multivehicle scenario is considered, wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information in a neighborwise fashion. To this end, a random-set-based local SLAM approach is undertaken at each vehicle by regarding the map as a random finite set and updating the first-order moment, called probability hypothesis density (PHD), of its multiobject density. Consensus on map PHDs is adopted in order to spread the map information through the team of vehicles also taking into account the different and time-varying fields of view of the team members. The convergence of the consensus strategy is analyzed theoretically, and the effectiveness of the proposed approach is assessed on both simulated and experimental datasets. The complexity and scalability of the proposed approach are also analyzed both theoretically and experimentally.

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.

The interest of this paper is to detect an unmanned aerial vehicle (UAV) and then initialize its trajectory, if it exists. The difficulties of detecting a UAV mainly lie in two aspects: a) small radar cross section (RCS), which causes extremely low signal-to-noise ratio (SNR); and b) low velocity, which results in weak Doppler effect. In this case, traditional track-before-detect (TBD) algorithms cannot achieve the satisfying probability of detection. In this paper, we propose to solve the problem of detecting a UAV based on micro-Doppler aided dynamic programming TBD (MA-DP-TBD) algorithm where the effect of micro-Doppler caused by the blades of UAV is taken into consideration to aid the detection process. The performance of proposed algorithm is examined via simulations.

This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed target detection and tracking over a peer-to-peer sensor network. A consensus Bernoulli filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when a suitable measure of discrepancy between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. Two information-theoretic criteria, i.e. Kullback-Leibler divergence and Hellinger distance, are adopted in order to measure the discrepancy between random finite set densities. The performance of the proposed event-triggered consensus Bernoulli filter is evaluated through simulation experiments.

This paper addresses the problem of target detection and tracking through a single frequency network with receiver position error. We consider the case that the SNR is low and it is hard to detect a target using the common detection algorithm like CFAR. The particle filter based track-before-detect algorithm is adopted to integrate the signal through sampling intervals to increase the SNR. The receiver is registered along with target detection and tracking. The performance of proposed algorithm is examined through simulations.

In this paper, consensus-based Kalman filtering is extended to deal with the problem of joint target tracking and sensor self-localization in a distributed wireless sensor network. The average weighted Kullback-Leibler divergence, which is a function of the unknown drift parameters, is employed as the cost to measure the discrepancy between the fused posterior distribution and the local distribution at each sensor. Further, a reasonable approximation of the cost is proposed and an online technique is introduced to minimize the approximated cost function with respect to the drift parameters stored in each node. The remarkable features of the proposed algorithm are that it needs no additional data exchanges, slightly increased memory space and computational load comparable to the standard consensus-based Kalman filter. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation experiments on both a tree network and a network with cycles as well as for both linear and nonlinear sensors.