分布式算法让多传感器协同追踪目标，减少通信开销。
Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks
- 各传感器本地处理，仅交换统计信息实现协作
- 采用快速并行采样提升推断精度与效率
- 适合动态连接、杂波变化的传感器网络
本文提出一种新型分布式期望传播算法，用于在杂波环境中实现多传感器、多目标追踪。该框架使每个传感器可本地运行，仅通过与其他传感器交换矩估计值进行协作，无需将全部数据传至中心节点。具体而言，引入一种快速且可并行的Rao-Blackwellised Gibbs采样方案，以逼近倾斜分布，从而提升期望传播更新的准确性和效率。实验结果表明，该算法在动态传感器连通性和不同杂波水平下，均能有效提升多目标追踪任务的通信与推断效率。
原文摘要 · Abstract (English)
In this paper, we present a novel distributed expectation propagation algorithm for multiple sensors, multiple objects tracking in cluttered environments. The proposed framework enables each sensor to operate locally while collaboratively exchanging moment estimates with other sensors, thus eliminating the need to transmit all data to a central processing node. Specifically, we introduce a fast and parallelisable Rao-Blackwellised Gibbs sampling scheme to approximate the tilted distributions, which enhances the accuracy and efficiency of expectation propagation updates. Results demonstrate that the proposed algorithm improves both communication and inference efficiency for multi-object tracking tasks with dynamic sensor connectivity and varying clutter levels.
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