Adaptive noise covariance PHD filter under nonlinear measurement

被引：0
|
作者：
Yuan C. [1 ]
Wang J. [1 ]
Xiang H. [1 ]
Wei S. [1 ]
Zhang Y. [1 ]
机构：
[1] School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing
来源：
Wang, Jun (wangj203@buaa.edu.cn) | 1600年 / Beijing University of Aeronautics and Astronautics (BUAA)卷 / 43期
基金：
中国国家自然科学基金;
关键词：
Multi-target tracking; Probability hypothesis density (PHD) filter; Random finite set; Unknown measurement noise covariance; Variational Bayesian (VB);
D O I：
10.13700/j.bh.1001-5965.2016.0034
中图分类号：
学科分类号：
摘要：
Probability hypothesis density (PHD) filter has been demonstrated to be an effective approach for multi-target tracking in real time. However, these methods based on the PHD filter assume that the measurement noise covariance is known as a priori. This is unrealistic for real applications because it may be previously unknown or its value may be time-varying as the environment changes. To solve this problem, an adaptive noise covariance algorithm for multi-target tracking under the nonlinear measurement is proposed. Based on the PHD filter, the proposed algorithm employs the cubature Kalman (CK) technology to approximate the nonlinear model, models the noise covariance distribution as inverse Wishart (IW) distribution, and recursively estimates the joint posterior density of the measurement noise covariance and multi-target states by the variational Bayesian (VB) approach. The simulation results indicate that the proposed algorithm could effectively estimate measurement noise covariance, and achieve the accurate estimation of the target number and corresponding multi-target states. © 2017, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：53 / 60
页数：7
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