Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint

Yu-tang ZHU; Yong-bo ZHAO; Jun LIU; Peng-lang SHUI

doi:10.1631/FITEE.1601112

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Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint

Regular Papers | Updated：2022-05-19

- Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint
- 基于正定约束广义秩信号模型的低复杂度稳健自适应算法
- Frontiers of Information Technology & Electronic Engineering Vol. 17, Issue 11, Pages: 1245-1252(2016)
- Affiliations：
  
  National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
- Author bio：
  
  E-mail:yutangzhu_xd@163.com
- Funds：
- DOI：10.1631/FITEE.1601112
  CLC： TN911.7
- Received：27 February 2016，
  
  Accepted：14 September 2016，
  
  Published：2016-11
- Accepted：
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Yu-tang ZHU, Yong-bo ZHAO, Jun LIU, et al. Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1245-1252.
DOI：

Yu-tang ZHU, Yong-bo ZHAO, Jun LIU, et al. Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1245-1252. DOI： 10.1631/FITEE.1601112.

摘要

本文针对广义秩信号模型提出一种低复杂度的稳健自适应波束形成算法，用来抑制期望信号的阵列响应失配和接收信号的协方差矩阵失配。该波束形成算法不仅考虑期望信号以及接收信号协方差矩阵不确定集的范数有界性，还保证期望信号协方差矩阵的正定性。最终，本文在最差性能优化准则下推出一个计算简单的闭式加权矢量。计算机仿真结果验证了所提波束形成算法的有效性和稳健性。

Abstract

We propose a low complexity robust beamforming method for the general-rank signal model

to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices

but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion

a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.

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references

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