Collaborative sensor management for multitarget tracking using decentralized markov decision processes

In this paper, we consider the problem of collaborative sensor management with particular application to using unmanned aerial vehicles (UAVs) for multitarget tracking. We study the problem of decentralized cooperative control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. In order to handle these issues, the problem is presented as a decentralized operation of a group of decision-makers lacking full observability of the global state of the system. Recent advances in solving special classes of decentralized Markov Decision Processes (Dec-MDPs) are incorporated into the solution. In these classes of Dec-MDPs, the agents' transitions and observations are independent. Also, the collaborating agents share common goals or objectives. Given the Dec-MDP model, a local policy of actions for a single agent (UAV) is given by a mapping from a current partial view of a global state observed by an agent to actions. The available probability model regarding possible and confirmed locations of the targets is considered in the computations of the UAVs' policies. Simulation results are presented on a representative multisensor-multitarget tracking problem.