Decentralized Search and Track with Multiple Autonomous Agents

In this paper we study the problem of cooperative searching and tracking (SAT) of multiple moving targets with a group of autonomous mobile agents that exhibit limited sensing capabilities. We assume that the actual number of targets is not known a priori and that target births/deaths can occur anywhere inside the surveillance region. For this reason efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we augment the classical Probability Hypothesis Density (PHD) filter with the ability to propagate in time the search density in addition to the target density. Based on this, we develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area. The performance of the proposed approach is demonstrated through simulation experiments.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. S. Papaioannou, P. Kolios, T. Theocharides, C. G. Panayiotou and M. M. Polycarpou, "Decentralized Search and Track with Multiple Autonomous Agents," 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, 2019, pp. 909-915. doi: 10.1109/CDC40024.2019.9029236 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9029236&isnumber=902885