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Distributed Multiple Hypothesis Tracker for Mobile Sensor Networks

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Distributed Autonomous Robotic Systems (DARS 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 28))

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This paper proposes a distributed estimation and control algorithm to allow a team of robots to search for and track an unknown number of targets. The number of targets in the area of interest varies over time as targets enter or leave, and there are many sources of sensing uncertainty, including false positive detections, false negative detections, and measurement noise. The robots use a novel distributed Multiple Hypothesis Tracker (MHT) to estimate both the number of targets and the states of each target. A key contribution is a new data association method that reallocates target tracks across the team. The distributed MHT is compared against another distributed multi-target tracker to test its utility for multi-robot, multi-target tracking.

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This work was funded by NSF grants IIS-1830419 and CNS-2143312.

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Correspondence to Philip Dames .

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Xin, P., Dames, P. (2024). Distributed Multiple Hypothesis Tracker for Mobile Sensor Networks. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham.

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