Sequential measurement-driven multi-target Bayesian filter

Zong-Xiang Liu,Liang-Qun Li,Wei-Xin Xie,Li-Juan Li

doi:10.1186/s13634-015-0228-8

Zong-Xiang Liu, Liang-Qun Li + Show 2 more

Open Access

https://doi.org/10.1186/s13634-015-0228-8

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Abstract

Bayesian filter is an efficient approach for multi-target tracking in the presence of clutter. Recently, considerable attention has been focused on probability hypothesis density (PHD) filter, which is an intensity approximation of the multi-target Bayesian filter. However, PHD filter is inapplicable to cases in which target detection probability is low. The use of this filter may result in a delay in data processing because it handles received measurements periodically, once every sampling period. To track multiple targets in the case of low detection probability and to handle received measurements in real time, we propose a sequential measurement-driven Bayesian filter. The proposed filter jointly propagates the marginal distributions and existence probabilities of each target in the filter recursion. We also present an implementation of the proposed filter for linear Gaussian models. Simulation results demonstrate that the proposed filter can more accurately track multiple targets than the Gaussian mixture PHD filter or cardinalized PHD filter.

Highlights

Multi-target tracking aims to detect individual targets in the surveillance region of interest and estimate their states according to a sequence of noisy and cluttered measurements collected by sensors
With the use of the Bayesian framework to propagate the posterior intensity of multiple targets recursively, the probability hypothesis density (PHD) filter provides a numerically tractable solution to this problem [2, 3]
To resolve the multi-target tracking problem efficiently in the case of low detection probability and to reduce data processing delay, we propose a sequential measurement-driven Bayesian filter

Summary

Introduction

Multi-target tracking aims to detect individual targets in the surveillance region of interest and estimate their states according to a sequence of noisy and cluttered measurements collected by sensors. To resolve the multi-target tracking problem efficiently in the case of low detection probability and to reduce data processing delay, we propose a sequential measurement-driven Bayesian filter. This filter propagates the marginal distributions and existence probabilities of each target in the filter recursion and uses received measurements to generate new marginal distributions and update existing marginal distributions. The proposed filter has a sufficient memory to missing targets, which enables this filter applicable to tracking multiple targets in the case of low detection probability This filter reduces the data processing delay that exists in PHD and CPHD filters because new incoming measurements can be processed whenever they become available.

Multi-target Bayesian filter

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Conclusions