Adaptive active noise control based on Renyi's quadratic entropy

The classical feedforward active noise control methods with mean-square error criteria only consider second order statistics of signals, but neglect real existing non-Gaussian signals. Therefore, these methods do not perform well for non-Gaussian noises. An adaptive finite impulse response(FIR) controller with filtered X algorithm based on Renyi's quadratic entropy is proposed to attenuate the noises. Renyi's quadratic entropy is defined as the performance index; and the probability density function of the system error is estimated by Parzen windowing estimation method. Renyi's quadratic entropy information gradient descent algorithm is applied to the adaptive FIR controller. In addition, the computational complexity and convergence of the proposed algorithm are analyzed. The simulations of single frequency signal and real non-Guassian broadband noises demonstrate that the proposed scheme can improve the non-Gaussian noises reduction performance.