通讯量无疑是很多分布式传感网一个重要的考量指标。网络协作算法的设计常常受限于此。

本文提出了一种最小节点间通讯量（邻居节点每次只需要相互传递一个实数的信息量）的分布式PHD滤波器实现分布式多目标跟踪。除了通讯量小之外，不同节点可以采用不同的PHD滤波器实现方式，比如高斯混合和粒子滤波。虽然通讯消耗小，但算法的精度收益显著。

详见：

T. Li, F. Hlawatsch and P. M. Djurić, "Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking," in IEEE Signal Processing Letters, vol. 26, no. 1, pp. 49-53, Jan. 2019.
doi: 10.1109/LSP.2018.2878064

基于模一致性的PHD滤波实现分布式多目标跟踪。 连接

Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking

Tiancheng Li  ; Franz Hlawatsch  ; Petar M. Djurić 

Abstract:

We present a distributed probability hypothesis density (PHD) filter for multitarget tracking in decentralized sensor networks with severely constrained communication. The proposed “cardinality consensus” (CC) scheme uses communication only to estimate the number of targets (or, the cardinality of the target set) in a distributed way. The CC scheme allows for different implementations—e.g., using Gaussian mixtures or particles—of the local PHD filters. Although the CC scheme requires only a small amount of communication and of fusion computation, our simulation results demonstrate large performance gains compared with noncooperative local PHD filters.

Published in: IEEE Signal Processing Letters ( Volume: 26 , Issue: 1 , Jan. 2019 )

Page(s): 49 - 53

DOI: 10.1109/LSP.2018.2878064