Recursive Maximum Correntropy Learning Algorithm With Adaptive Kernel Size

In this brief, a robust correntropy-based adaptive learning algorithm, called the adaptive kernel recursive maximum correntropy criterion, is proposed by considering an adaptive kernel size based on the Kullback-Leibler divergence minimization. Simulation results confirm that the proposed algorithm can perform better than other adaptive algorithms especially when the environment is disturbed by non-Gaussian noises. The proposed algorithm is beneficial for using in helicopters, since the pilot speech signal is contaminated by the acoustic impulsive noises that are originated from rotor blades.