Multi-sensor control for multi-object Bayes filters

Abstract

Sensor management in multi-object stochastic systems is a theoretically and computationally challenging problem. This paper presents a novel approach to the multi-target multi-sensor control problem within the partially observed Markov decision process (POMDP) framework. We model the multi-object state as a labeled multi-Bernoulli random finite set (RFS), and use the labeled multi-Bernoulli filter in conjunction with minimizing a task-driven control objective function: posterior expected error of cardinality and state (PEECS). A major contribution is a guided search for multi-dimensional optimization in the multi-sensor control command space, using coordinate descent method. In conjunction with the Generalized Covariance Intersection method for multi-sensor fusion, a fast multi-sensor algorithm is achieved. Numerical studies are presented in several scenarios where numerous controllable (mobile) sensors track multiple moving targets with different levels of observability. The results show that our method works significantly faster than the approach taken by a state of art method, with similar tracking errors. Index Termspartially observed Markov decision process, multi-target tracking, random finite sets, labeled multi-Bernoulli filter, coordinate descent.

Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a δ-GLMB Filter

“…The first algorithm is proposed in [ 25 ], while the last three algorithms are proposed in this paper. As in [ 27 ] and [ 28 ], the GCI weights used in the CS divergence with exhaustive search were set to the same. By convention, the random control method was still chosen as a standard comparison object to verify the effectiveness of other sensor control algorithms.…”

“…(1) So far, there are only two multi-sensor control methods for MTT within the RFS framework: the CS divergence based algorithm [ 27 ] and the PEECS-based algorithm [ 28 ]. Although the two algorithms have different objective functions for multi-sensor control, their performance is very similar since both methods are based on the distributed processing architecture and have to apply the GCI rule to obtain the multi-sensor posterior density.…”

“…However, the control complexity will increase significantly for the multi-sensor cases. To the best of our knowledge, there are only two methods for the multi-sensor control until now, which are the maximization of Cauchy-Schwarz (CS) divergence in [ 27 ] and the minimization of posterior expected error of cardinality and states (PEECS) in [ 28 ]. Both methods are based on the distributed processing architecture, so the rule of generalized covariance intersection (GCI) [ 29 , 30 ] has to be used to obtain the multi-sensor posterior density by the fusion of the local posterior densities of all individual sensor nodes.…”

“…In addition, although the commands of the multi-sensor control problem can be found by exhaustive search method, its computation cost will increase significantly with the increase of the number of sensors. The coordinate descent method [ 31 ] was applied by Wang et al [ 28 ] to reduce the computation cost by finding a suboptimal multi-sensor control command. But this method can only be used for the unconstrained multi-sensor control problem.…”

Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects

“…RFS-POMDP provides a natural framework that addresses all the challenges of our online UAV path planning problem. Indeed, RFS-POMDP for multiple objects tracking with various information theoretic reward functions and task-based reward functions have been proposed in [13]- [17] and [18]- [20], respectively. This framework accommodates path planning for tracking an unknown and time-varying a number of objects in a conceptually intuitive manner.…”