Multi-Sensor Control for Multi-Object Bayes Filters
For researchers in multi-target tracking, this work provides a computationally efficient solution to multi-sensor control, though the improvement is incremental over existing methods.
This paper addresses the multi-sensor control problem for multi-object Bayes filters by proposing a fast algorithm that uses coordinate descent for multi-dimensional optimization and Generalized Covariance Intersection for sensor fusion. The method achieves significantly faster computation than a state-of-the-art approach while maintaining similar tracking errors.
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.