  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.
