Robust Constrained CMA Based on a Bayesian Approach under Quadratic Constraint

Abstract

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, we 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. It is found that robust constrained CMA under quadratic constraint can reduce successfully the output power of internal noise while cancelling the interference enough. The Lagrange multipliers are updated and added at each step. The proposed algorithm offers faster rate, has better interference suppression, and yields higher SINR and better signal capture performance than the constrained CMA. Via computer simulation, it is show that the proposed algorithm achieves a substantially improved performance.