Collaborative distributed sensor management and information exchange flow control for multitarget tracking using Markov decision processes

In this paper, we consider the problem of collaborative management of uninhabited aerial vehicles (UAVs) for multitarget tracking. In addition to providing a solution to the problem of controlling individual UAVs, we present a method for controlling the information flow among them. The latter provides a solution to one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. The problem of decentralized cooperative control considered in this paper is an optimization of the information obtained by a number of UAVs, carrying out surveillance over a region, which includes a number of confirmed and suspected moving targets with the goal to track confirmed targets and detects new targets in the area. Each UAV has to decide on the most optimal path with the objective to track as many targets as possible, maximizing the information obtained during its operation with the maximum possible accuracy at the lowest possible cost. Limited communication between UAVs and 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 an operation of a group of decision makers. Markov Decision Processes (MDPs) are incorporated into the solution. A decision mechanism for collaborative distributed data fusion provides each UAV with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of UAVs that are decentralized, heterogenous, and potentially unreliable. Simulation results are presented on a representative multisensor-multitarget tracking problem.