Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking

In this paper, we consider the cooperative decision-making problem for multi-target tracking in multi-unmanned aerial vehicle (UAV) systems. The multi-UAV decision-making problem is modeled in the framework of distributed multi-agent partially observable Markov decision processes (MPOMDPs). Specifically, the state of the targets is represented by the joint multi-target probability distribution (JMTPD), which is estimated by a distributed information fusion strategy. In the information fusion process, the most accurate estimation is selected to propagate through the whole network in finite time. We propose a max-consensus protocol to guarantee the consistency of the JMTPD. It is proven that the max-consensus can be achieved in the connected communication graph after a limited number of iterations. Based on the consistent JMTPD, the distributed partially observable Markov decision algorithm is used to make tracking decisions. The proposed method uses the Fisher information to bid for targets in a distributed auction. The bid is based upon the reward value of the individual UAV's POMDPs, thereby removing the need to optimize the global reward in the MPOMDPs. Finally, the cooperative decision-making approach is deployed in a simulation of a multi-target tracking problem. We compare our proposed algorithm with the centralized method and the greedy approach. The simulation results show that the proposed distributed method has a similar performance to the centralized method, and outperforms the greedy approach.