Hierarchical markov decision processes based distributed data fusion and collaborative sensor management for multitarget multisensor tracking applications

Abstract

This paper presents a decision mechanism based on hierarchical Markov decision processes as a solution for two important problems in multitarget multisensor tracking - distributed data fusion and collaborative sensor management. In application to the distributed data fusion, this paper presents a hierarchical multi-level decision mechanism for collaborative distributed data fusion that provides each platform 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 platforms that are decentralized, heterogenous, and potentially unreliable. In application to collaborative sensor management, this paper studies the problem of decentralized cooperative control of a group of unmanned aerial vehicles (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. Simulation examples demonstrate the operation and the performance results.