Distributed Multitarget Tracking Research Articles

Resource Allocation for Distributed Multitarget Tracking in Radar Networks With Missing Data

Resource Allocation for Distributed Multitarget Tracking in Radar Networks With Missing Data

Event-Triggered Consensus LMB Filter for Distributed Multitarget Tracking

This correspondence investigates the problem of reducing energy and bandwidth consumption of a sensor network for distributed multitarget tracking by the labeled multi-Bernoulli filter. To this end, an event-triggered method is adopted together with a consensus strategy for each sensor node to transmit only Bernoulli components (BCs) that achieve enough information gain. Two message transmission strategies, i.e., joint and independent transmission, are devised. The possibility to separately trigger BCs allows the proposed method to better tradeoff tracking performance versus communication load with respect to existing distributed multitarget tracking approaches.

Asynchronous Non–uniform Distributed Multi–target Tracking Filter Based on Asymmetric Alpha–divergence Consensus

With more and more extensive application of target tracking, distributed multi–target tracking (DMT) becomes an important research direction. However, the synchronization characteristics of some sensor networks can not be guaranteed due to communication delay, non–uniform sampling and so on. Therefore, a DMT algorithm for asynchronous non–uniform multi–sensor networks is studied in this paper. Firstly, by using the cardinalized probability hypothesis density (CPHD) based on continuous–discrete multi–target dynamic (CD) model (CD-CPHD), a CD–CPHD algorithm with multi–step birth process and time–triggered structure (TCD–CPHD) is proposed; Secondly, an asymmetric alpha–divergence (AAD) based adaptive computing structure of time–trigger index is designed to reduce the communication rate of TCD–CPHD; Thirdly, the fusion rule of AAD consensus is derived to construct the fusion process of the proposed algorithm; Finally, by combining the above structures and the fusion rule, the implementation process of the proposed algorithm is given. Theoretical analysis and exhaustive experimental analysis show the effectiveness of the proposed algorithm.

A TOA-DOA Hybrid Factor Graph-Based Technique for Multi-Target Geolocation and Tracking

In this article, we propose a new distributed sensors-based multi-target geolocation and tracking technique. The proposed technique is a joint time-of-arrival (TOA) and direction-of-arrival (DOA) factor graph (FG) for multi-target geolocation (FG-GE), which is further combined with another FG for extend Kalman filtering (FG-GE-EKF) for tracking. Two-dimensional (2D) and three-dimensional (3D) scenarios are considered. In the FG-GE part, a new sensor association technique is proposed to solve the matching problem, which makes the right correspondence between the DOA/TOA information gathered by the distributed sensors and each target. With the proposed sensor association technique, the measured signals from targets can adequately be matched to their corresponding FGs. Thereby, the multi-target geolocation can be reduced to multiple independent single target geolocation. In addition, in the 3D scenario, each target is projected onto three orthogonal planes in the $(x,y,z)$ coordinate. With this operation, the 3D geolocation is decomposed into three 2D geolocation problems. In the FG-GE-EKF part, the whole tracking system can be divided into two steps: prediction step and update step. In the prediction step, the predicted state is obtained from the previous state. Then, we utilize the predicted state as a prior information, and also to update the message exchanged in FG-GE. In the update step, the estimates obtained by FG-GE are regarded as observation state which is used to refine the predicted state, and acquire the current state. With proposed the FG-GE-EKF, the position estimation accuracy and tracking performance can be improved dramatically, without requiring excessively high computational effort.

Resource Scheduling for Distributed Multi-Target Tracking in Netted Colocated MIMO Radar Systems

In this paper, an effective solution is proposed for joint beam and power scheduling (JBPS) in the netted Colocated MIMO (C-MIMO) radar systems for distributed multi-target tracking (MTT). At its core, the proposed solution includes a distributed fusion architecture that reduces the communication requirements while maintaining the overall robustness of the system. The distributed fusion architecture employs the covariance intersection (CI) fusion to address the unknown information correlations among radar nodes. Each C-MIMO radar node in the network can generate a time-varying number of beams with controllable transmitting power by waveform synthesis, thus is capable of accomplishing multiple tracking tasks simultaneously. To maximize the global MTT performance of the radar network, the proposed JBPS solution implements an online resource scheduling, regarding both the generated beams and the transmitted power of all radar nodes, based on the feedback of the MTT results. A scaled accuracy-based objective function is designed to quantify the global MTT performance while properly taking into account different target priorities on resource allocation. The Bayesian Cramer-Rao lower bound (BCRLB) for CI fusion rule is derived and utilized as the constituent of the objective function since it provides a lower bound on the accuracy of the target state estimates. As the formulated JBPS problem is non-convex, we propose a fast reward-based iterative descending approach to solve it effectively. Numerical results show that the proposed JBPS can deliver superior performance in terms of maximizing the overall MTT performance while possessing high flexibility on the resource allocation regarding different target priorities.

Fusion of Labeled RFS Densities With Minimum Information Loss

This paper addresses fusion of labeled random finite set (LRFS) densities according to the criterion of minimum information loss (MIL). The MIL criterion amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs) with the fused density appearing as righthand argument of the KLDs. In order to ensure the fused density to be consistent with the local ones when LRFS densities are marginal $\delta$-generalized labeled multi-Bernoulli (M$\delta$-GLMB) or labeled multi-Bernoulli (LMB) densities, the MIL rule is further elaborated by imposing the constraint that the fused density be in the same family of local ones. In order to deal with different fields-of-view (FoVs) of the local densities, the global label space is divided into disjoint subspaces which represent the exclusive FoVs and the common FoV of the agents, and each local density is decomposed into the sub-densities defined in the corresponding subspaces. Then fusion is performed subspace-by-subspace to combine local subdensities into global ones, and the global density is obtained by multiplying the global sub-densities. Further, in order to tackle the label mismatching issue arising in practical applications, a rank assignment optimization (RAO) of a suitably defined cost is carried out so as to match labels from different agents. Moreover, issues concerning implementation of the MIL rule and its application to distributed multitarget tracking (DMT) are discussed. Finally, the performance of the proposed fusion approach is assessed via simulation experiments considering DMT with either the same or different FoVs of the agents.

Event-Triggered Distributed Multitarget Tracking

This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed multitarget tracking over a peer-to-peer sensor network. A consensus cardinalized probability hypothesis density (CCPHD) filter with event-triggered communication is developed by enforcing each node to broadcast its local information to the neighbors only when it is worth to, i.e., the node has gained a sufficient amount of information with respect to its latest broadcasting. To this end, each sensor node separately evaluates the discrepancies of the cardinality probability mass function (PMF) and of the spatial probability density function (PDF) between the current local posterior and the one recoverable from neighbors after the latest transmission. Then, each sensor node selectively sends the specific information on the multitarget distribution (i.e., the cardinality PMF or the spatial PDF or both) that is considered to be worth transmitting (i.e., such that the respective discrepancy exceeds a preset threshold). Two types of discrepancy measures, i.e., the Kullback–Leibler divergence and the Cauchy–Schwarz divergence, are investigated. The performance of the proposed event-triggered CCPHD filter is evaluated through simulation experiments.

Distributed multi-sensor multi-view fusion based on generalized covariance intersection

Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed, named CA-GCI fusion algorithm. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of L1 error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirm the effectiveness of the proposed CA-GCI fusion algorithm.

Distributed Multitarget Tracking Based on Diffusion Strategies Over Sensor Networks

We consider the distributed multitarget tracking over sensor networks, where each node only communicates with its neighbors. We develop a diffusion-based distributed multisensor multitarget tracking algorithm. The state update of the diffusion-based distributed algorithm is mainly composed of two phases: an adaptation phase and a combination phase. During the adaptation phase, each node updates its local estimate by using all its neighbors' measurements. It is achieved based on a multi-sensor cardinalized probability hypothesis density filter. During the combination phase, each node fuses all its neighbors' local estimates. It is achieved based on a generalized version of covariance intersection technique. Compared to the consensus-based distributed algorithm, the proposed algorithm has two advantages. First, it can provide more accurate and robust tracking results, especially when the detection probability that the sensors detect the targets is low. Second, it has lower communication load because the consensus iterations are not required. Numerical results are provided to illustrate the performance of the proposed algorithm.

Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking

We present a distributed probability hypothesis density (PHD) filter for multitarget tracking in decentralized sensor networks with severely constrained communication. The proposed “cardinality consensus” (CC) scheme uses communication only to estimate the number of targets (or, the cardinality of the target set) in a distributed way. The CC scheme allows for different implementations—e.g., using Gaussian mixtures or particles—of the local PHD filters. Although the CC scheme requires only a small amount of communication and of fusion computation, our simulation results demonstrate large performance gains compared with noncooperative local PHD filters.

Consensus CPHD Filter for Distributed Multitarget Tracking

The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios.

Optimization-Based Dynamic Sensor Management for Distributed Multitarget Tracking

In this paper, the general problem of dynamic assignment of sensors to local fusion centers (LFCs) in a distributed tracking framework is considered. With technological advances, a large number of sensors can be deployed for multitarget tracking purposes. However, due to physical limitations such as frequency, power, bandwidth, and fusion center capacity, only a limited number of them can be used by each LFC. The transmission power of future sensors is anticipated to be software controllable within certain lower and upper limits. Thus, the frequency reusability and the sensor reachability can be improved by controlling transmission powers. Then, the problem is to select the sensor subsets that should be used by each LFC and to find their transmission frequencies and powers in order to maximize the tracking accuracies and minimize the total power consumption. The frequency channel limitation and the advantage of variable transmitting power have not been discussed in the literature. In this paper, the optimal formulation for the aforementioned sensor management problem is provided based on the posterior Cramer-Rao lower bound. Finding the optimal solution to the aforementioned NP-hard multiobjective mixed-integer optimization problem in real time is difficult in large-scale scenarios. An algorithm is presented to find a suboptimal solution in real time by decomposing the original problem into subproblems, which are easier to solve, without using simplistic clustering algorithms that are typically used. Simulation results illustrating the performance of sensor array manager are also presented.

Distributed Multitarget Tracking and Identity Management

The problem of tracking multiple targets and managing their identities in sensor networks is considered. Each sensor is assumed to have its own surveillance region and an ability to communicate with its neighboring sensors. We propose a scalable, distributed, multitarget-tracking and identity-management algorithm that can track an unknown number of targets and manage their identities efficiently in a distributed sensor network environment. Distributed multitarget tracking and identity management finds a globally consistent solution by maintaining local consistency among neighboring sensors. Distributed multitarget tracking and identity management consists of data association, multitarget tracking, identity management, and identity and track fusion. The data-association and multitarget-tracking problems are efficiently solved by Markov chain Monte Carlo data association, which can track an unknown number of targets. Distributed multitarget tracking and identity management manages identities of targets based on the identity-mass-flow framework. This framework prevents exponential growth in computation and storage of target-to-track association probabilities. Using identity and track fusion, distributed multitarget tracking and identity management maintains consistent identities and tracks among neighboring sensors. The performance and features of distributed multitarget tracking and identity management are extensively evaluated in simulation.