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Robust Constrained CMA Based on a Bayesian Approach Under Quadratic Constraint

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Executive Summary

CMA has been known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. But in practical applications, the constrained CMA degrades in the presence of both signal steering vector errors and interference nonstationarity. In this paper, the authors propose robust constrained CMA based on a Bayesian approach under the quadratic constraint, which improves the output performance in nonideal situations. The quadratic constraint on the weight can provide excellent robustness to signal steering vector mismatches and to random perturbations in sensor parameters

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