Research on the Robustness of Array Beamforming based on Support Vector Regression

摘要：

In practical array systems, traditional adaptive beamforming algorithms will degrade if some unexpected assumptions become wrong or imprecise. Therefore, the robustness of adaptive beamforming techniques against environmental and array imperfections is one of the key issues. Compared with traditional methods, robust beamforming brings about much improvement on array performance. In this paper, we propose a new approach to adaptive beamforming that provides increased robustness against the mismatch problems. After generalizing the conventional linearly constrained minimum variance cost function by including a regularization term to penalize differences between the actual and the ideal array responses, we adopt the form of Support Vector Regression (SVR) to increase the beamformer robustness against errors by utilizing ε-insensitive loss function for the penalty term. In comparison with other robust beamforming techniques especially for high SNR scenarios, simulation results show that the proposed SVR-based beamformer has the desired robust performance both in no-mismatch and mismatch situations regardless of the number of interference signals.

关键词： Robustness Adaptive array Beamforming Support vector regression Steering vector uncertainty

作者: Guancheng Lin Yaan Li Beili Jin Beili Jin1

作者单位: College of Marine,Northwestern Polytechnical University Department of Media and Communications Engin College of Marine,Northwestern Polytechnical University

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）