一种基于稀疏约束的稳健波束形成方法

doi:10.3969/j.issn.0372-2112.2016.09.036

电子学报 ›› 2016, Vol. 44 ›› Issue (9): 2276-2281.DOI: 10.3969/j.issn.0372-2112.2016.09.036

一种基于稀疏约束的稳健波束形成方法

解虎^1,2, 冯大政², 袁明冬²

1. 中国空间技术研究院西安分院西安, 陕西西安 710100;
2. 西安电子科技大学雷达信号处理国家重点实验室, 陕西西安 710071

收稿日期:2014-10-23 修回日期:2015-12-30 出版日期:2016-09-25
- 作者简介:
- 解虎男,1987年生于陕西渭南,中国空间技术研究院西安分院博士后,研究方向为阵列信号处理和空时自适应信号处理.E-ail:xiehumor@163.com;冯大政 男,1959年生于陕西安康,西安电子科技大学电子工程学院教授,博士生导师.研究方向为雷达信号处理、信号参数估计、仿大脑信息处理、场景感知、模式识别等.
- 基金资助:
- 国家自然科学基金 (No.61271293）

A Robust Beamforming Method Based on Sparse Constraint

XIE Hu^1,2, FENG Da-zheng², YUAN Ming-dong²

1. China Academy of Space Technology, Xi'an Branch, Xi'an, Shaanxi 710100, China;
2. National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi 710071, China

Received:2014-10-23 Revised:2015-12-30 Online:2016-09-25 Published:2016-09-25
- Supported by:
- National Natural Science Foundation of China (No.61271293)

摘要/Abstract

摘要：

通过分析最优自适应波束形成权矢量的子空间组成，发现最优权仅位于低维的干扰加信号子空间中.一般系统所要抑制的干扰数目远小于系统自由度，因此一旦估计出干扰空间和信号导向矢量，只需求解一个低维的组合矢量即可求得自适应权矢量，同时也极大地降低了计算复杂度.本文首先构造一个完备的干扰加信号子空间（IPSS），然后对组合矢量进行稀疏约束，找到一组列数最小的信号加干扰子空间来构造自适应权.仿真实验验证了所提算法的有效性和稳健性.

关键词: 稳健波束形成, 稀疏约束, 导向矢量失配, 正则化, 凸优化

Abstract:

By analyzing the constitution of the optimal adaptive weight,we find that the optimal adaptive weight only lies in a low-dimension subspace spanned by the desired signal steering vector and the interferences subspace.Generally,the number of interferences designed to suppress is much smaller than that of the array sensors.Consequently,once the interference-plus-signal subspace (IPSS) is estimated,only a low-dimension combination vector is needed to compute,which leads to a reduction of the computation complexity.First,we construct a complete IPSS.And then the sparse constraint is imposed on the combination vector to select the least number of column vectors of the complete IPSS to form the adaptive weight.Simulation results validate the effectiveness and robustness of the proposed algorithm.

Key words: robust beamforming, sparse constraint, steering vector mismatch, regularization, convex programming

中图分类号:

TN957

解虎, 冯大政, 袁明冬. 一种基于稀疏约束的稳健波束形成方法[J]. 电子学报, 2016, 44(9): 2276-2281.

XIE Hu, FENG Da-zheng, YUAN Ming-dong . A Robust Beamforming Method Based on Sparse Constraint[J]. Acta Electronica Sinica, 2016, 44(9): 2276-2281.

参考文献

[1] Gong C,Huang L,Xu D,et al.Knowledge-aided robust adaptive beamforming with small snapshots[J].Electronics Letters,2013,49(20): 1258-1259.
[2] 杜江,朱柯.智能麦克风阵列语音分离和说话人跟踪技术研究[J].电子学报,2005,33(2): 382-384. Du Jiang,Zhu Ke.Smart micro phone arrays for speech sources separation and speakertracking[J].Acta Electronic Sinica,2005,33(2): 382-384.(in Chinese)
[3] Aryafar E,Khojastepour M A,Sundaresan K,et al.ADAM: An adaptive beamforming system for multicasting in wireless LANs[J].IEEE/ACM Transactions on Networking (TON),2013,21(5): 1595-1608.
[4] Vorobyov S A.Principles of minimum variance robust adaptive beamforming design[J].Signal Processing,2013,93(12): 3264-3277.
[5] 陈明建,罗景青.基于双不确定集约束的稳健宽带波束形成算法[J].电子学报,2014,42(3): 587-591. M J Chen,J Q LUO.Robust broadband beamforming algorithm under the double uncertainty set constraints[J].Acta Electronic Sinica,2014,42(3): 587-591.(in Chinese)
[6] Olivier B,Stéphanie B.Adaptive processing with signal contaminatedtraining samples[J].IEEE Trans Signal Process,2013,61(17):4318-4329.
[7] Gu YJ,Leshem A.Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation[J].IEEE Trans Signal Process,2012,60(7): 3881-3885.
[8] SVorobyov,A Gershman,ZQ Luo.Robust adaptive beamforming using worst-case performance optimization:a solution to the signal mismatch problem[J].IEEE Trans Signal Process,2003,51(2):313-324.
[9] Jian L,Petre S,ZSWang.On robust capon beamforming and diagonal loading[J].IEEE Trans on Signal Process,2003,51(7):1702-1715.
[10] Dong M,Zheng Q,Su H.Automatic robust adaptive beamforming via ridge regression using l 1-norm approximation[A].2013 International Conference on Radar[C].Adelaide,Australia:IEEE,2013.385-387.
[11] C Y Chen,P P Vaidyanathan.Quadratically constrained beamforming robust against direction of arrival mismatch[J].IEEE Trans on Signal Process,2007,55(8):4139-4150.
[12] Van Trees H L.Detection,Estimation,and Modulation Theory,Optimum Array Processing[M].New York:John Wiley & Sons,2004.710-887.
[13] C D Meyer.Matrix Analysis and Applied Linear Algebra[M].Philadelphia:SIAM,2000.115-124.
[14] Hassanien A,Vorobyov S A,Wong K M.Robust adaptive beamforming using sequential quadratic programming: An iterative solution to the mismatch problem[J].IEEE Signal Processing Letters,2008,15:733-736.
[15] Khabbazibasmenj A,Vorobyov S A,Hassanien A.Robust adaptive beamforming based on steering vector estimation with as little as possible prior information[J].IEEE Trans on Signal Process,2012,60(6):2974-2978.
[16] CVX Research,Inc.CVX: Matlab Software For Disciplined Convex Programming[CP].Version 2.0.http://cvxr.com/cvx,2011.
[17] B.D.Carlson.Covariance matrix estimation errors and diagonal loading in adaptive arrays[J].IEEE Trans Aerosp Electron Syst,1988,24(4): 397-401.

一种基于稀疏约束的稳健波束形成方法

A Robust Beamforming Method Based on Sparse Constraint

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