A User Parameter-Free Robust Adaptive Beamformer Based on General Linear Combination in Tandem with Steering Vector Estimation

  • Authors:

  • Wei Jin;Weimin Jia;Fenggan Zhang;Minli Yao

  • Affiliations:

  • Department of Electronic and Information Engineering, Xi'an Research Institute of High Technology, Xi'an, People's Republic of China 710025;Department of Electronic and Information Engineering, Xi'an Research Institute of High Technology, Xi'an, People's Republic of China 710025;Department of Electronic and Information Engineering, Xi'an Research Institute of High Technology, Xi'an, People's Republic of China 710025;Department of Electronic and Information Engineering, Xi'an Research Institute of High Technology, Xi'an, People's Republic of China 710025

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2014

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Abstract

If there is a mismatch between the assumed steering vector (SV) and the real value, the performance of adaptive beamforming methods is degraded. When the signal SV is known exactly but the sample size is small, the performance degradation can also occur. The second kind of degradation is mainly due to the mismatch between the sample covariance matrix and the real one. Almost all existing robust adaptive beamformers are proposed to improve the robustness against these two types of mismatch. Indeed, most of them are user parameter dependent, and the user parameter-free robust beamformers are scarce. As one of the shrinkage methods, the general linear combination (GLC) based beamformer is a good user parameter-free robust beamformer. However, it is only suitable for the scenarios with low sample size and/or small SV mismatch. In this paper, we propose a new robust beamformer, and it is based on general linear combination in tandem with SV estimation (GLCSVE). The proposed approach is superior to GLC in two aspects. One is that the GLCSVE beamformer performs well not only with small but also with large sample size. The other is that the GLCSVE can effectively deal with a large range of SV mismatch. Moreover, the proposed GLCSVE approach is a user parameter-free robust beamformer, and is more suitable for application than the parameter dependent approaches. The idea of our method can also be used to enhance other shrinkage based beamformers.