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This research advances distributed state estimation by modifying particle filters to prevent overconfidence in autonomous surface vessel teams operating with intermittent connectivity. The proposed augmentation strategically spreads particles during communication gaps, improving tracking robustness without sacrificing nominal performance in multi-agent maritime scenarios.
A Distributed Consensus Particle Filter for Target Tracking using Autonomous Surface Vessels
arXiv:2609.09066v1 Announce Type: new Abstract: Maritime target tracking over large distances often requires multi-agent teams without centralized coordination, and intermittent communication. Each agent must maintain an independent estimate that can take advantage of opportunistic communications availability when possible. This can lead to overly confident local estimates in the absence of external data. In this work, we propose an augmentation to a classical particle filter implementation that accounts for this potential source of error by forcing particles to spread strategically in the absence of informative updates from other sensor nodes. We demonstrate our method using Unmanned Surface Vessels (USVs) on a lake, and show that our augmentations do not deteriorate nominal performance, and provide an advantage in some specific edge cases.
Abstract:Maritime target tracking over large distances often requires multi-agent teams without centralized coordination, and intermittent communication. Each agent must maintain an independent estimate that can take advantage of opportunistic communications availability when possible. This can lead to overly confident local estimates in the absence of external data. In this work, we propose an augmentation to a classical particle filter implementation that accounts for this potential source of error by forcing particles to spread strategically in the absence of informative updates from other sensor nodes. We demonstrate our method using Unmanned Surface Vessels (USVs) on a lake, and show that our augmentations do not deteriorate nominal performance, and provide an advantage in some specific edge cases.
Robotics (cs.RO)
arXiv:2609.09066 [cs.RO]
(or arXiv:2609.09066v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.09066 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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