有源欺骗干扰环境下的DOA估计

王珊珊; 刘峥; 谢荣; 冉磊

doi:10.11999/JEIT180488

有源欺骗干扰环境下的DOA估计

doi: 10.11999/JEIT180488

西安电子科技大学雷达信号处理国家重点实验室西安 710071

基金项目: 博士后创新人才支持计划(BX20180240)

详细信息

作者简介:
王珊珊：女，1996年生，博士生，研究方向为认知雷达信号处理技术

刘峥：男，1964年生，教授，博士生导师，研究方向为雷达信号处理的理论与系统设计、雷达精确制导技术、多传感器信息融合等

谢荣：男，1982年生，副教授，研究方向为阵列信号处理、雷达天线理论等

冉磊：男，1989年生，博士后，研究方向为雷达成像技术

通讯作者:
刘峥　lz@xidian.edu.cn

中图分类号: TN911.7
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- 被引次数: 0
出版历程
- 收稿日期: 2018-05-21
- 修回日期: 2019-01-08
- 网络出版日期: 2019-01-25
- 刊出日期: 2019-05-01

DOA Estimation Under Active Deception Jamming Environment

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Postdoctoral Program for Inno-vative Talents(BX20180240)

摘要

摘要:
针对有源欺骗干扰环境下基于小样本的DOA估计问题，该文提出自适应极化滤波(APF)联合块稀疏贝叶斯学习(BSBL)算法的DOA估计方法。首先，通过APF抑制干扰能量，提高信干比。然后，建立有源欺骗干扰环境下的稀疏贝叶斯模型，基于相邻快拍相关性，利用BSBL算法进行DOA估计。仿真和实测数据处理结果表明，所提方法降低了干扰对BSBL算法的影响，且与APF联合子空间类算法或最大似然算法(ML)相比，具有更高的空间分辨率和DOA估计精度。
- DOA估计 /
- 抗干扰 /
- 自适应极化滤波 /
- 块稀疏贝叶斯学习
Abstract:
For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. First, the interference energy is suppressed using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. The target DOA is estimated using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.
- DOA estimation /
- Anti-jamming /
- Adaptive polarization filter /
- Sparse Bayesian learning

HTML全文

图 1 APF联合BSBL算法的测角性能分析

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图 2 样本数为5时DOA估计结果图

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图 3 干扰场景图

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图 4 干扰和弹载雷达方位角和仰角分布图

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图 5 实测数据测角均方根误差图

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