Noise-Free Maximum Correntropy Criterion Algorithm in Non-Gaussian Environment

In this brief, a noise-free maximum correntropy criterion (NFMCC) algorithm is proposed for system identification in non-Gaussian environments. The proposed algorithm utilizes correntropy theory to construct a cost function which is realized based on a normalized Gaussian kernel. In addition, a new dynamic step size scheme is proposed to enhance the performance of the proposed algorithm, which is implemented by minimizing the noise-free a posteriori error signal, and the mean square deviation (MSD) is greatly decreased. The proposed NFMCC algorithm shows significant property in reducing the detrimental effects of outliers and impulsive noise with different input signals. Moreover, a Students' T distributed noise is employed to evaluate the effectiveness of the proposed algorithm in terms of the MSD and convergence for heavy tailed noising environment. The parameter effects on the NFMCC algorithm are also presented, and its performance is investigated on a real-life channel that is measured in underwater. Simulation results prove the effectiveness of the proposed algorithm which provides a considerable computational complexity and an acceptable running time.