In this paper, we present a resilient detection algorithm for multiple targets in a distributed environment with limited information sharing. The problem setup is as follows. There are M agents (detectors/sensors), which will be used to collaboratively detect the behaviors of N targets. The number of agents is much smaller than that of targets (i.e., M << N). Targets are assumed to be located in a 2D environment (The extension to 3D is straightforward). Each agent has a limited sensing/communication range and can only detect a small group of targets in its sensing range. Agents only maintain a strongly connected communication topology at certain time intervals and each agent can communicate with its neighboring agents about their situation of target detection. The proposed distributed detection algorithm is based on consensus theory. The resilience of the proposed detection algorithm is verified through extensive simulations under four different scenarios: (1) agents with limited sensing/limited communication capabilities; (2) the existence of unexpected agent failure; (3) the existence of unexpected communication link dropout; and (4) the situation with intermittent communications. The proposed design provides a new solution for control and estimation of unmanned autonomous systems.
Jing Wang, In Soo Ahn, Yufeng Lu, Tianyu Yang, and Gennady Staskevich, "Resilient detection of multiple targets using a distributed algorithm with limited information sharing," Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520D (Presented at SPIE Defense + Security: April 17, 2018; Published: 9 May 2018); https://doi.org/10.1117/12.2304471.
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