Constrained least lncosh adaptive filtering algorithm

•We have proposed a new constrained least lncosh (CLL) adaptive filtering algorithm for combating different impulsive noises.•The proposed CLL algorithm is developed via incorporating an lncosh function into constrained optimization problem under non-Gaussian noise environment.•The performance of the CLL algorithm has been analyzed for identifying systems and its convergence behavior and theoretical steady-state mean-square-deviation are 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. 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.