最差性能最优的稳健波束形成算法

doi:10.3969/j.issn.1001-2400.2010.01.001

J4 ›› 2010, Vol. 37 ›› Issue (1): 1-7+17.doi: 10.3969/j.issn.1001-2400.2010.01.001

• 研究论文 • 下一篇

最差性能最优的稳健波束形成算法

刘聪锋;廖桂生

(西安电子科技大学雷达信号处理重点实验室，陕西西安 710071)

收稿日期:2008-11-17 出版日期:2010-02-20 发布日期:2010-03-29
通讯作者: 刘聪锋
作者简介:刘聪锋(1973-)，男，副教授，博士，E-mail: cfliu@mail.xidian.edu.cn．
基金资助:
国家杰出青年基金资助项目(60825104);国家自然科学基金资助项目(60736009);博士后基金资助项目(20090451251);陕西省工业攻关资助项目(2009k08-31)

Robust beamforming algorithm using worse-case performance optimization

LIU Cong-feng;LIAO Gui-sheng

(Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an 710071， China)

Received:2008-11-17 Online:2010-02-20 Published:2010-03-29
Contact: LIU Cong-feng

摘要/Abstract

摘要：

基于最差性能最优的稳健波束形成算法可以等价转换成加载样本矩阵求逆(LSMI)算法．提出了一种新的求解方法，准确地计算出Lagrange乘数，给出了LSMI算法中的最优加载量，解决了对角加载技术中加载量估计难题．理论分析和计算机仿真表明，具有最优负加载的LSMI波束形成算法具有最优的性能改善，模约束参数选取得越大，性能改善越接近于最优．

关键词: 自适应波束形成, 导向矢量失配, 最差性能最优, 负对角加载

Abstract:

The robust adaptive beamformer using worse-case performance optimization can be converted to the loaded sample matrix inversion (LSMI) algorithm equivalently. For the LSMI beamformer, a novel method is developed to solve the precise Lagrange multiplier, and the optimal loading level can be computed exactly. Most important, this method gives the efficient solution to finding the optimal loading level for diagonal loading. Theoretical analysis and simulation indicate that the LSMI beamformer with the optimal negative loading can give the best performance improvement, and the constraint parameter is so selected that the larger it is the closer to optimization the improvement will be.

Key words: adaptive beamforming, steering vector mismatch, worst-case performance optimization, negative diagonal loading

刘聪锋;廖桂生. 最差性能最优的稳健波束形成算法[J]. J4, 2010, 37(1): 1-7+17.

LIU Cong-feng;LIAO Gui-sheng. Robust beamforming algorithm using worse-case performance optimization[J]. J4, 2010, 37(1): 1-7+17.

参考文献

［1］Feldman D D, Griffiths L J. A Projection Approach to Robust Adaptive Beamforming［J］. IEEE Trans on Signal Processing, 1994, 42(4): 867-876.
［2］Guerci J R. Theory and Application of Covariance Matrix Tapers for Robust Adaptive Beamforming［J］. IEEE Trans on Signal Processing, 1999, 47(4): 997-985.
［3］Carlson B D. Covariance Matrix Estimation Errors and Diagonal Loading in Adaptive Arrays［J］. IEEE Trans on Aerosp Electron Syst, 1988, 24(7): 397-401.
［4］Tian Z, Bell K L, Van Trees H L. A Recursive Least Squares Implementation for LCMP Beamforming under Quadratic Constraint［J］. IEEE Trans on Signal Processing, 2001, 49(6): 1138-1145.
［5］Elnashar A, Elnoubi S M, El-Mikati H A. Further Study on Robust Adaptive Beamforming With Optimum Diagonal Loading［J］. IEEE Trans on Antennas Propagation, 2006, 54(12): 3647-3658.
［6］Li Jian, Stotica P, Wang Zhisong. On Robust Capon Beamformer and Diagonal Loading［J］. IEEE Trans on Signal Processing, 2003, 51(7): 1702-1715.
［7］Li Jian, Stotica P, Wang Zhisong. Doubly Constrained Robust Capon Beamformer［J］. IEEE Trans on Signal Processing, 2004, 52(9): 2407-2423.
［8］Shahbazpanahi S, Gershman A B, Luo Zhiquan, et al. Robust Adaptive Beamforming for General-rank Signal Models［J］. IEEE Trans on Signal Processing, 2003, 51(9): 2257-2269.
［9］Vorobyov S A, Gershman A B, Luo Zhiquan. Robust Adaptive Beamforming Using Worst-case Performance Optimization: a Solution to the Signal Mismatch Problem［J］. IEEE Trans on Signal Processing, 2003, 51(2): 313-324.
［10］Oh J S, Kim S J, Hsiung K L. A Computationally Efficient Method for Robust Minimum Variance Beamforming［C］//Vehicular Technology Conference 2005: Vol 2. Stockholm: IEEE, 2005: 1162-1165.
［11］Robert G L, Stephen P B. Robust Minimum Variance Beamforming［J］. IEEE Trans on Signal Processing, 2005, 53(5): 1684-1696.
［12］Cesar C G, Ignacio S, Javier V, et al. Robust Array Beamforming with Sidelobe Control Using Support Vector Machines［J］. IEEE Trans on Signal Processing, 2007, 55(2): 574-585.
［13］Mutapcic A, Kim S J, Boyd B. Beamforming with Uncertain Weights［J］. IEEE Signal Processing Letter, 2007, 14(5): 348-351.
［14］Chen C Y, Vaidyanathan P P. Quadratically Constrained Beamforming Robust Against Direction-of-Arrival Mismatch［J］. IEEE Trans on Signal Processing, 2007, 55(8): 4139-4150.
［15］Eldar Y C, Nehorai A, La Rosa P S. A Competitive Mean-squared Error Approach to Beamforming［J］. IEEE Trans on Signal Processing, 2007, 55(11): 5143-5154.

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最差性能最优的稳健波束形成算法

Robust beamforming algorithm using worse-case performance optimization

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