导向矢量和协方差矩阵联合迭代估计的稳健波束形成算法

杨志伟; 张攀; 陈颖; 许华健

doi:10.11999/JEIT180225

导向矢量和协方差矩阵联合迭代估计的稳健波束形成算法

doi: 10.11999/JEIT180225

杨志伟^{1, 2, ,},
张攀¹,
陈颖¹,
许华健¹

1.
西安电子科技大学雷达信号处理国家重点实验室西安 710071
2.
西安电子科技大学信息感知协同创新中心西安 710071

基金项目: 国家自然科学基金(61671352)，国家青年科学基金(61501471)，上海航天科技创新基金(SAST2016027，SAST2016033)，教育部“认知无线电与信息处理”重点实验室基金(CRKL160206)

详细信息

作者简介:
杨志伟：男，1980年生，副教授，主要研究方向为阵列信号处理、地面动目标检测、极化处理

张攀：男，1993年生，硕士生，研究方向为阵列信号处理

陈颖：女，1993年生，硕士生，研究方向为阵列信号处理

许华健：男，1990年生，博士生，研究方向为空时自适应处理、合成孔径雷达动目标检测

通讯作者:
杨志伟　 yangzw@xidian.edu.cn

中图分类号: TN911.7
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出版历程
- 收稿日期: 2018-03-09
- 修回日期: 2018-07-24
- 网络出版日期: 2018-08-06
- 刊出日期: 2018-12-01

Steering Vector and Covariance Matrix Joint Iterative Estimations for Robust Beamforming

Zhiwei YANG^{1, 2
, ,},
Pan ZHANG¹,
Ying CHEN¹,
Huajian XU¹

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61671352), The National Science Foundation for Young Scientists of China (61501471), Shanghai Aerospace Science and Technology Innovation Foundation (SAST2016027, SAST2016033), The Foundation of Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (CRKL160206)

摘要

摘要: 针对自适应波束形成器在目标导向矢量存在约束偏差时性能急剧下降的问题，该文提出一种目标导向矢量和干扰噪声协方差矩阵联合迭代估计的稳健波束形成算法。该算法首先采用稀疏重构的方法得到目标导向矢量的初始值，并通过从采样协方差矩阵中剔除目标信号估计值完成干扰加噪声协方差矩阵的初始化；然后在建立导向矢量误差优化模型的基础上，采用凸优化方法对目标导向矢量和干扰加噪声协方差矩阵联合迭代求解。最后利用目标导向矢量和干扰加噪声协方差矩阵的稳态估计值获得自适应权矢量。仿真结果表明该算法提高了波束形成器在目标导向矢量约束偏差时的输出信干噪比。
- 自适应波束形成 /
- 导向矢量约束偏差 /
- 联合迭代 /
- 凸优化
Abstract: Focusing on the problem of adaptive beamformer performance decreasing due to target steering vector constraint errors, an algorithm for robust beamforming with joint iterative estimations of steering vector and covariance matrix is proposed. First, the initial value of target steering vector is obtained by sparse reconstruction, following eliminating the target signal estimation in the sampling covariance matrix, the initialization of the covariance matrix is completed; Then, basing on the steering vector error optimization model, this algorithm adopts the convex optimization to estimate joint-iteratively target steering vector and interference plus noise covariance matrix. Finally, the adaptive weight vector is obtained with the steady estimations of steering vector and covariance matrix. Simulation results show output signal to interference and noise ratio is improved in the situation of target steering vector constraint errors.
- Adaptive beamforming /
- Steering vector constraint errors /
- Joint iteration /
- Convex optimization

HTML全文

图 1 联合迭代估计流程图

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图 2 目标来波方向误差下性能对比

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图 3 目标来波方向误差和阵元位置误差下性能对比

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图 4 目标来波方向误差和幅度相位误差下输出SINR随输入SNR变化曲线

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