Distributed Bernoulli Filtering for Target Detection and Tracking Based on Arithmetic Average Fusion

Publisher: IEEE

Author(s)

Tiancheng Li ; Zhunga Liu ; Quan Pan

Abstract:

We present a distributed Bernoulli filter for tracking a target that may be present or absent in the cluttered surveillance area in unknown time intervals by using a decentralized sensor network. As a key feature of the Bernoulli filter, a parameter referring to the target existence probability is online updated jointly with the target state probability density function. We propose to fuse them in parallel, both in an arithmetic average fusion manner via the existing consensus or flooding scheme. Alternatively, one may communicate and fuse merely target existence probabilities, leading to a communication-inexpensive protocol. We experimentally compare the proposed approaches, based on the Gaussian mixture implementation, with the cutting-edge geometric average fusion approach based on a Doppler shift sensor network, demonstrating advantages in computing efficiency and in dealing with local missed detection.

Published in: IEEE Signal Processing Letters

DOI: 10.1109/LSP.2019.2950588

本文推广笔者之前的算术均值-随机有限集融合 AA-RFS fusion 工作，首次将算术均值AA融合推广和应用于分布式伯努利滤波器设计，获得一种可完全解析实现的分布式随机集滤波器。另外可完全解析实现的分布式随机集滤波器是 AA-PHD （基于GM或SMC实现）滤波器，是笔者前期工作。而目前主流的几何均值GA融合，虽然对PHD/Bernoulli可闭合求解，但是对于GM和SMC并不能严格解析计算，更不用说更复杂的MB、(G)LMB等（对于其，AA和GA都不能闭合求解），也就是目前的分布式随机有限集滤波器多数都是基于近似均值融合的。只有AA-PHD/Bernoulli-GM/SMC 才是严格的可完全解析实现的。

AA融合相比于GA至少三个方面的明确优势：

计算快而稳，算法实现简单
可有效应对漏检和近邻目标
更加适合大规模传感网（Clustering 就是一种AA fusion）

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