Statistical Analysis of Maximum Correntropy Criterion Subband Adaptive Filtering Algorithm

The correntropy contains all the even-order information of data and is well suited for dealing with non-Gaussian noise. The subband adaptive filtering algorithm combined with the maximum correntropy criterion (MCC-SAF) has been proposed to efficiently handle colored input signals and impulsive noise. Compared with other similar algorithms, the MCC-SAF algorithm exhibits faster convergence speed and lower steady-state error in non-Gaussian noise environments. However, to the best of our knowledge, the transient and steady-state statistical performance of this algorithm is still missing. In this letter, the mean-square performance of the MCC-SAF is analyzed under Gaussian, binary, and uniform noises using vectorization method, in which the mathematical expectation of the exponential term is computed by means of the moment generating function (MGF), and finally the theoretical prediction models for mean square deviation (MSD) are derived and verified by computer simulations.