谢文冲2,
袁华东2,
段克清3,
王永良2
1.海军工程大学电子工程学院 武汉 430033
2.空军预警学院 武汉 430019
3.中山大学电子与通信工程学院 广州 510006
基金项目:国家自然科学基金(61871397)
详细信息
作者简介:许红：男，1991年生，博士生，研究方向为雷达数据处理
谢文冲：男，1978年生，副教授，主要研究方向为机载雷达信号处理、空时自适应信号处理等
袁华东：男，1985年生，博士生，研究方向为雷达数据处理、阵列信号处理
段克清：男，1981年生，副教授，主要研究方向为空时自适应信号处理、阵列信号处理等
王永良：男，1965年生，教授，主要研究方向为雷达信号处理、空时自适应信号处理等
通讯作者:许红 xuhongzhxu@163.com
中图分类号:TN957.51; TP391.41计量
文章访问数:622
HTML全文浏览量:259
PDF下载量:50
被引次数:0
出版历程
收稿日期:2019-07-10
修回日期:2020-02-28
网络出版日期:2020-09-01
刊出日期:2020-11-16
Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model
Hong XU1, 2,,,Wenchong XIE2,
Huadong YUAN2,
Keqing DUAN3,
Yongliang WANG2
1. College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
2. Air Force Early Warning Academy, Wuhan 430019, China
3. School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China
Funds:The National Natural Science Foundation of China (61871397)
摘要
摘要:现有的增广状态-交互式多模型算法存在着依赖于量测噪声协方差矩阵这一先验信息的问题。当先验信息未知或不准确时，算法的跟踪性能将会下降。针对上述问题，该文提出一种自适应的变分贝叶斯增广状态-交互式多模型算法VB-AS-IMM。首先，针对增广状态的跳变马尔科夫系统，该文给出了联合估计增广状态和量测噪声协方差矩阵的变分贝叶斯推断概率模型。其次，通过理论推导证明了该概率模型是非共轭的。最后，通过引入一种“信息反馈+后处理”方案，提出联合后验密度的次优求解方法。所提算法能够在线估计未知的量测噪声协方差矩阵，具有更强的鲁棒性和适应性。仿真结果验证了算法的有效性。
关键词:机动目标跟踪/
交互式多模型/
增广状态/
变分贝叶斯/
自适应滤波
Abstract:The existing Augmented State-Interracting Multiple Model (AS-IMM) algorithm suffers from the problem that it relies on the prior information of the covariance matrix of the measurement noise. When the prior information is unavailable or inaccurate, the tracking performance of AS-IMM will be degraded. In order to overcome this problem, a novel adaptive Bayesian Variational Augmented State-Interracting Multiple Model (VB-AS-IMM) algorithm is proposed. Firstly, the variational Bayesian inference probabilistic model of the augmented state and the covariance matrix of the measurement noise for the jump Markovarian system is presented. Secondly, the probabilistic model is proven to be non-conjugated. Finally, by introducing a novel post processing method, the suboptimal solution to calculate the joint posterior distribution is proposed. The proposed algorithm can estimate the unknown covariance matrix of the measurement noise online, thus it is more robust and has higher adaptability. Simulation result verifies good performance of the proposed algorithm.
Key words:Maneuvering target tracking/
Interracting multiple model/
Augmented state/
Variational Bayesian/
Adaptive filtering
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