来磊,
骆艳卜,
李伟
空军工程大学信息与导航学院 西安 710077
基金项目:国家自然科学基金(61571456, 61603409)，博士后基金(2017M623352, 2018T111148)
详细信息
作者简介:邹鲲：邹 鲲(1976–)，男，副教授，研究方向为统计信号处理，信号检测与估计，认知雷达信号处理
来磊：来 磊(1983–)，男，讲师，研究方向为UAV智能导航，集群协同
骆艳卜(1980–)，男，讲师，研究方向为无线电导航信号处理，雷达信号处理
李伟：李 伟(1978–)，男，副教授，研究方向为新体制雷达技术
通讯作者:邹鲲 wyyxzk@163.com
责任主编：赵拥军 Corresponding Editor: ZHAO Yongjun中图分类号:TN957.51
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被引次数:0
出版历程
收稿日期:2019-04-18
修回日期:2019-11-25
网络出版日期:2019-12-16
Suppression of Non-Gaussian Clutter from Subspace Interference
ZOU Kun,,LAI Lei,
LUO Yanbo,
LI Wei
School of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
Funds:The National Natural Science Foundation of China (61571456, 61603409), The Postdoctoral Science Foundation of China (2017M623352, 2018T111148)
More Information
Corresponding author:ZOU Kun, wyyxzk@163.com
摘要
摘要:在复杂电磁环境下，往往需要在线估计杂波协方差矩阵，从而自适应调整滤波器权值，实现对杂波的有效抑制，这样有利于目标的估计、检测、定位或跟踪。该文考虑非高斯杂波模型，且部分杂波受到子空间信号干扰，并且有用信号也位于该子空间内。常规方法会导致自适应滤波器在目标多普勒频率处有较大的衰减，极大影响了有用信号的探测。为此提出了一种知识辅助的分层贝叶斯模型，采用变分贝叶斯推断方法获得杂波协方差矩阵的近似后验分布，利用后验均值设计杂波抑制滤波器，可以有效提高目标的探测性能。计算机仿真和实测数据验证结果表明，该方法能够有效抑制杂波，而在目标处有较好的探测能力。
关键词:非高斯杂波/
子空间干扰/
分层贝叶斯模型/
变分贝叶斯推断/
杂波抑制
Abstract:In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance.
Key words:Non-Gaussian clutter/
Subspace interference/
Hierarchical Bayesian model/
Variational Bayesian inference/
Clutter suppression
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