Constant Modulus Beamformer Based on Probability Density Function Matching for Impulsive Stable Noise

To reducing the degradation of the beamforming caused by the impulsive stable noise, a new constant modulus (CM) algorithm was proposed based on the maximum matching of the probability density functions between the desired signal and output signal inspired by the information adaptive learning. The Parzen kernel method was utilized for the probability density function estimation and the weight of the beamforming was updated by the stochastic steepest gradient. Computer simulations show that the proposed CM algorithm has higher output signal to interfere noise ratio and faster convergence than the conventional CM beamforming in stable distribution noise.