Multitarget tracking in sensor networks via efficient information-theoretic sensor selection

In networks composed of moving robots or static sensing nodes, multitarget tracking is critical and fundamental for high-level applications, such as scene analysis or event detection. However, tracking multiple targets in the sensor network is challenging for two reasons: multisensor multitarget fusion itself is difficult and dynamic sensor scheduling is necessary to balance the tracking accuracy and energy consumption of the sensor network. In this article, we present a novel information-theoretic sensor selection method for multitarget tracking via the multi-Bernoulli filter. The sensor selection is based on the multi-Bernoulli filtering and a collection of subselection problems for individual target to avoid the combinatorial optimization. A subselection problem for each target is investigated under the framework of partially observed Markov decision process, and we propose to solve it by maximizing the information gain of the probability hypothesis density. Simulation results validate the effectiveness and efficiency of our method for multitarget tracking in sensor networks.