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Robust partially adaptive array processing based on generalized sidelobe canceller

Fei Huang, Weixing Sheng, Xiaofeng Ma

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Abstract

A novel robust partially adaptive array processing based on generalized sidelobe canceller(GSC) is presented in this paper. In this technique, the dimensional transformation is obtained from the unconstraint adaptive weight vector of GSC. It is constructed from eigenvectors of the signal-plus-interference. By combining the partially adaptive beamformer with the projection approach, the robust partially adaptive beamforming algorithm based on GSC is proposed. The proposed technique is not sensitive to the array errors, as well as it can save the computation load due to the reduced rank adaptive weight. Simulations results illustrate the effectiveness of this approach.

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What this paper is about

A novel robust partially adaptive array processing based on generalized sidelobe canceller(GSC) is presented in this paper. In this technique, the dimensional transformation is obtained from the unconstraint adaptive weight vector of GSC. It is constructed from eigenvectors of the signal-plus-interference. By combining the partially adaptive beamformer with the projection approach, the robust partially adaptive beamforming algorithm based on GSC is proposed. The proposed technique is not sensitive to the array errors, as well as it can save the computation load due to the reduced rank adaptive weight. Simulations results illustrate the effectiveness of this approach.

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Available abstract

A novel robust partially adaptive array processing based on generalized sidelobe canceller(GSC) is presented in this paper. In this technique, the dimensional transformation is obtained from the unconstraint adaptive weight vector of GSC. It is constructed from eigenvectors of the signal-plus-interference. By combining the partially adaptive beamformer with the projection approach, the robust partially adaptive beamforming algorithm based on GSC is proposed. The proposed technique is not sensitive to the array errors, as well as it can save the computation load due to the reduced rank adaptive weight. Simulations results illustrate the effectiveness of this approach.

Key concepts: Adaptive beamformer, Array processing, Computation, Signal processing, Computer science, Algorithm, Adaptive filter, Weight

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