2016•Unpublished venueRequires access

The robustness research of beamforming algorithm against pointing error

Cui Lin, Jiao Yameng

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Abstract

In this paper a robust beamforming method is proposed, which can effectively overcome the influence of DOA(direction of arrival) mismatch. The proposed method exhibits the enhanced robustness of the beamformers based on analysis of linear constrained minimum variance beamformer. Then the support vector regression (SVR) algorithm is applied to the robust beamforming, which is based on the principle of structural risk minimization. Simulation results show that the SVR-based beamforming method enhances the robustness in terms of desired signal array manifold vector errors in an ideal scenario of no-mismatch and an actual scenario of mismatch respectively.

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What this paper is about

In this paper a robust beamforming method is proposed, which can effectively overcome the influence of DOA(direction of arrival) mismatch. The proposed method exhibits the enhanced robustness of the beamformers based on analysis of linear constrained minimum variance beamformer. Then the support vector regression (SVR) algorithm is applied to the robust beamforming, which is based on the principle of structural risk minimization. Simulation results show that the SVR-based beamforming method enhances the robustness in terms of desired signal array manifold vector errors in an ideal scenario of no-mismatch and an actual scenario of mismatch respectively.

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Available abstract

In this paper a robust beamforming method is proposed, which can effectively overcome the influence of DOA(direction of arrival) mismatch. The proposed method exhibits the enhanced robustness of the beamformers based on analysis of linear constrained minimum variance beamformer. Then the support vector regression (SVR) algorithm is applied to the robust beamforming, which is based on the principle of structural risk minimization. Simulation results show that the SVR-based beamforming method enhances the robustness in terms of desired signal array manifold vector errors in an ideal scenario of no-mismatch and an actual scenario of mismatch respectively.

Key concepts: Robustness (evolution), Beamforming, Computer science, Algorithm, Minification, Support vector machine, Adaptive beamformer, Minimum-variance unbiased estimator

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