多艘无人船在无中心协调下实现远程目标跟踪，提升局部估计鲁棒性。
A Distributed Consensus Particle Filter for Target Tracking using Autonomous Surface Vessels
- 基于粒子滤波的分布式共识机制，通过策略性扩散粒子应对通信中断
- 实测表明性能不降，且在通信稀疏场景中显著提升跟踪精度
- 适用于无人船、无人机等异构移动传感器网络的协同感知
远距离海上目标跟踪常需无中心协调的多智能体团队及间歇性通信。各智能体必须独立维护估计值，并在通信可用时利用机会性信息。这可能导致缺乏外部数据时局部估计过于自信。本文提出对经典粒子滤波的改进方法，在缺乏来自其他传感器节点的有效更新时，主动策略性地扩展粒子分布以抑制误差。我们在湖上使用无人水面艇（USVs）验证该方法，结果表明该增强不会降低常规性能，且在特定边缘情况下表现更优。
原文摘要 · Abstract (English)
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.
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