多智能体协作追踪目标，提升定位精度。
Collaborative State Fusion in Partially Known Multi-agent Environments
- 分两阶段融合：先用卡尔曼滤波估计状态，再通过可学习权重加权融合。
- 在4个智能体2个目标场景下，融合增益比现有方法高9.1%。
- 适合传感器有误差、环境部分未知的协同追踪系统。
本文研究多智能体环境中移动目标的协作状态融合问题。由于机载传感器感知范围有限且存在误差，需聚合各智能体观测以实现更优的目标状态估计。现有方法因依赖完全已知的先验状态空间模型，且易受单个传感器观测异常值影响，表现不佳。为此，提出两阶段协作融合框架——可学习加权鲁棒融合（LoF）。LoF结合局部状态估计算法（如卡尔曼滤波）与可学习权重生成器，缓解先验模型与目标运动模式间的不匹配。针对观测异常值，设计时序软中位数（TSM）方案实现鲁棒融合。在协作探测仿真环境中评估，结果表明：在4个智能体、2个目标的设置下，LoF相比最先进方法提升9.1%的融合增益。
原文摘要 · Abstract (English)
In this paper, we study the collaborative state fusion problem in a multi-agent environment, where mobile agents collaborate to track movable targets. Due to the limited sensing range and potential errors of on-board sensors, it is necessary to aggregate individual observations to provide target state fusion for better target state estimation. Existing schemes do not perform well due to (1) impractical assumption of the fully known prior target state-space model and (2) observation outliers from individual sensors. To address the issues, we propose a two-stage collaborative fusion framework, namely \underline{L}earnable Weighted R\underline{o}bust \underline{F}usion (\textsf{LoF}). \textsf{LoF} combines a local state estimator (e.g., Kalman Filter) with a learnable weight generator to address the mismatch between the prior state-space model and underlying patterns of moving targets. Moreover, given observation outliers, we develop a time-series soft medoid(TSM) scheme to perform robust fusion. We evaluate \textsf{LoF} in a collaborative detection simulation environment with promising results. In an example setting with 4 agents and 2 targets, \textsf{LoF} leads to a 9.1\% higher fusion gain compared to the state-of-the-art.
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