基于ET-PHD滤波器和变分贝叶斯近似的扩展目标跟踪算法

doi:10.11772/j.issn.1001-9081.2020040451

计算机应用 ›› 2020, Vol. 40 ›› Issue (12): 3701-3706.DOI: 10.11772/j.issn.1001-9081.2020040451

• 应用前沿、交叉与综合 • 上一篇

基于ET-PHD滤波器和变分贝叶斯近似的扩展目标跟踪算法

何祥宇¹, 李静², 杨数强¹, 夏玉杰¹

1. 洛阳师范学院物理与电子信息学院, 河南洛阳 471934;
2. 洛阳师范学院信息技术学院, 河南洛阳 471934

收稿日期:2020-04-12 修回日期:2020-08-03 出版日期:2020-12-10 发布日期:2020-09-15
通讯作者: 何祥宇(1982-),男,河南泌阳人,讲师,博士,主要研究方向:多目标跟踪。xyuhe@foxmail.com
作者简介:李静(1983-),女,河南泌阳人,讲师,博士,主要研究方向:信号处理;杨数强(1979-),男,河南洛阳人,副教授,博士研究生,主要研究方向:信号处理;夏玉杰(1978-),男,河南洛阳人,副教授,博士,主要研究方向:通信信号处理
基金资助:
河南省高等学校青年骨干教师培养计划（2017GGJS136，2018GGJS126）。

Extended target tracking algorithm based on ET-PHD filter and variational Bayesian approximation

HE Xiangyu¹, LI Jing², YANG Shuqiang¹, XIA Yujie¹

1. College of Physical and Electronic Information, Luoyang Normal University, Luoyang Henan 471934, China;
2. School of Information Technology, Luoyang Normal University, Luoyang Henan 471934, China

Received:2020-04-12 Revised:2020-08-03 Online:2020-12-10 Published:2020-09-15
Supported by:
This work is partially supported by the Training Project for Young Backbone Teachers in Higher Education Institutions of Henan Province （2017GGJS136， 2018GGJS126）.

摘要/Abstract

摘要： 针对未知测量噪声协方差情况下的多扩展目标跟踪问题，利用扩展目标概率假设密度（ET-PHD）滤波器和变分贝叶斯（VB）近似理论，提出了一种标准ET-PHD滤波器的扩展方法及其解析的实现方法。首先，根据标准ET-PHD滤波器的目标状态方程和测量方程，定义了目标状态和测量噪声协方差的增广状态变量及二者的联合转移函数；然后，根据标准ET-PHD滤波器，构建了扩展的ET-PHD滤波器的预测和更新公式；最后，在线性高斯假设的条件下，利用高斯和逆伽马（IG）混合分布表示目标的联合后验强度函数，从而给出了扩展ET-PHD滤波器的解析实现。仿真结果表明：所提算法能提供可靠的跟踪结果，可有效地处理未知测量噪声协方差环境中的多扩展目标跟踪问题。

关键词: 扩展目标跟踪, 概率假设密度, 随机有限集, 变分贝叶斯, 噪声协方差

Abstract: Aiming at the tracking problem of multiple extended targets under the circumstances with unknown measurement noise covariance, an extension of standard Extended Target Probability Hypothesis Density (ET-PHD) filter and the way to realize its analysis were proposed by using ET-PHD filter and Variational Bayesian (VB) approximation theory. Firstly, on the basis of the target state equations and measurement equations of the standard ET-PHD filter, the augmented state variables of target state and measurement noise covariance as well as the joint transition function of the above variables were defined. Then, the prediction and update equations of the extended ET-PHD filter were established based on the standard ET-PHD filter. And finally, under the condition of linear Gaussian assumptions, the joint posterior intensity function was expressed as the Gaussian and Inverse-Gamma (IG) mixture distribution, and the analysis of the extended ET-PHD filter was realized. Simulation results demonstrate that the proposed algorithm can obtain reliable tracking results, and can effectively track multiple extended targets in the circumstances with unknown measurement noise covariance.

Key words: extended target tracking, Probability Hypothesis Density (PHD), Random Finite Set (RFS), Variational Bayesian (VB), noise covariance

中图分类号:

TP391

何祥宇, 李静, 杨数强, 夏玉杰. 基于ET-PHD滤波器和变分贝叶斯近似的扩展目标跟踪算法[J]. 计算机应用, 2020, 40(12): 3701-3706.

HE Xiangyu, LI Jing, YANG Shuqiang, XIA Yujie. Extended target tracking algorithm based on ET-PHD filter and variational Bayesian approximation[J]. Journal of Computer Applications, 2020, 40(12): 3701-3706.

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基于ET-PHD滤波器和变分贝叶斯近似的扩展目标跟踪算法

Extended target tracking algorithm based on ET-PHD filter and variational Bayesian approximation

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