Passive Multi-target Tracking Research Articles

Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D Passive Multitarget Tracking

This article considers the 3-D collaborative trajectory optimization (CTO) of multiple unmanned aerial vehicles to improve multitarget tracking performance with an asynchronous angle of arrival measurements. The predicted conditional Cramér–Rao lower bound is adopted as a performance measure to predict and subsequently control tracking error online. Then, the CTO problem is cast as a time-varying nonconvex problem subjected to constraints arising from dynamic and security (height, collision, and obstacle/target/threat avoidance). Finally, a comprehensive solution method (CSM) is presented to tackle the resulting problem, according to its unique structures. Specifically, if all security constraints are inactive, the CTO can be simplified as a nonconvex problem with convex dynamic constraints, which can be solved by the nonmonotone spectral projected gradient (NSPG) method. Oppositely, an alternating direction penalty method (ADPM) is presented to solve the CTO problem with some positive security constraints. The ADPM introduces auxiliary vectors to decouple the complex constraints and separates the CTO into several subproblems and tackles them alternately, while locally adjusting the penalty factor at each iteration. We show the subproblem w.r.t. the position vector is nonconvex but with convex constraints, which can be efficiently solved by the NSPG method. The subproblems w.r.t. the auxiliary vectors are separable and have closed-form solutions. Simulation results demonstrate that the CSM outperforms the unoptimized method in terms of tracking performance. Besides, the CSM achieves the near-optimal performance provided by the genetic algorithm with much lower computational complexity.

Decomposed POMDP Optimization-Based Sensor Management for Multi-Target Tracking in Passive Multi-Sensor Systems

This paper presents an efficient information-theoretic sensor management method to maximize the performance of the passive multi-sensor system for multi-target tracking. We model the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set and formulate the dynamic sensor selection process as a partially observable Markov decision process (POMDP). The optimization objective is to maximum the information gain obtained from the observed data, which is measured by the Cauchy-Schwarz divergence. This is accomplished with two main technical innovations. The first is a tractable decomposed POMDP based sensor selection solution, in which the informative sensors are selected sequentially from the candidates based on the Cauchy-Schwarz divergence. The second is a novel dual-stage multi-sensor fusion strategy based on the iterated-corrector GLMB filter. Since the uncertainty of the passive multi-sensor system is generally large, the performance of the iterated-corrector scheme can be greatly influenced by the order of sensor updates. To fix this problem, the selected sensors are ranked in order of the Cauchy-Schwarz divergence obtained in sensor selection, followed by the iterated-corrector update. For the multi-sensor fusion, the effect of poor performance sensors is weakened and that of better performance sensors is enhanced. Simulation studies demonstrate the effectiveness and efficiency of the proposed method in challenging passive multi-target tracking scenarios.

MDA-Based Data Association with Prior Track Information for Passive Multitarget Tracking

An assignment-based solution for the data association problem in synchronous passive multisensor (Type 3) tracking systems involves two steps: first measurement-to-measurement or static association is solved using a multidimensional (S-dimensional or S-D with S sensors) assignment, and then measurement-to-track association is solved using a 2-D assignment. This solution is computationally very expensive and, to rectify an efficient (S+1)-D assignment algorithm has been proposed in the literature. Two new assignment-based algorithms are proposed that use prior track information (i.e., predicted state and covariance) which result in improved tracking performance compared with the existing solutions, while requiring considerably less computations. One of the proposed algorithms, the gated assignment, is similar to the two-step solution mentioned above except that it uses prior track information and avoids the need to consider all possible association hypotheses in the static association step. The second algorithm, the gated (S+1)-D assignment, combines the gated assignment and the (S+1)-D algorithms. An approximation to the (S+1)-D algorithm is also derived when sensor measurements are independent, which results in an extremely fast solution. Simulation results confirm that the proposed algorithms show improved tracking performance and faster execution times.

Particle swarm optimization algorithm for passive multi-target tracking

In order to improve the performance of particle data association filter algorithm for multi-target tracking, an improved multi-target tracking algorithm is proposed in this paper. Particle swarm optimization (PSO) technology is introduced in the particle filter (PF) algorithm and a new best fitness function of particles is defined to better approximate the true posterior distribution, fuzzy clustering technique is combined for data association in the proposed algorithm. Simulation results show that the proposed algorithm has better performance than the conventional particle data association filter algorithm for passive multi-target tracking.