IEEE Transactions on Signal Processing
Theory and application of covariance matrix tapers for robustadaptive beamforming
IEEE Transactions on Signal Processing
A Bayesian approach to robust adaptive beamforming
IEEE Transactions on Signal Processing
Robust adaptive beamforming for general-rank signal models
IEEE Transactions on Signal Processing
The performance of matched-field beamformers with Mediterraneanvertical array data
IEEE Transactions on Signal Processing
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In practical problems, the constrained least square constant modulus algorithm (LSCMA) can suffer significant performance degradation in the presence of the slight mismatches between the actual and presumed array responses to the desired signal. In this paper, a novel robust constrained LSCMA is proposed based on explicit modeling of uncertainties in the desired signal array response. The proposed algorithm provides excellent robustness against the signal steering vector mismatches, enhances the array system performance in the perturbation situation and makes the mean output array SINR consistently close to the optimal one. Computer simulations demonstrate a better performance gain of the proposed algorithm compared as linear constrained LSCMA algorithm.