Constrained least lncosh adaptive filtering algorithm

Abstract

We propose a constrained least lncosh (CLL) adaptive filtering algorithm, which, as we show, provides better performance than other algorithms in impulsive noise environment. The proposed CLL algorithm is derived via incorporating a lncosh function in a constrained optimization problem under non-Gaussian noise environment. The lncosh cost function is a natural logarithm of a hyperbolic cosine function, and it can be considered as a combination of mean-square error and mean-absolute-error criteria. The theoretical analysis of convergence and steady-state mean-squared-deviation of the CLL algorithm in identification scenarios is presented. The theoretical analysis agrees well with simulation results and these results verify that the CLL algorithm possesses superior performance and higher robustness than other CAF algorithms under various non-Gaussian impulsive noises.