Efficient DOA estimation based on variable least Lncosh algorithm under impulsive noise interferences

The Lncosh function, a natural logarithm function that composes of hyperbolic cosine function, is thought of a compromise distribution between mean-absolute-error (MAE) and mean-square-error (MSE) via a regularization factor r (r > 0), which will provide potential superior performance in both Gaussian and impulsive noise environments. In this paper, a new DOA estimation algorithm is developed based on adaptive nulling technology and variable-parameter adaptive algorithm that is realized to reconstruct Lncosh function to modify least Lncosh (LL) algorithm to implement efficient DOAs for a wider range of applications. In the proposed algorithm, variable r-parameter and variable step-size schemes are devised to improve the LL algorithm. The DOA estimation capacity, root-mean-square error and mean stability for the proposed improved LL algorithm are analyzed under various noises including impulsive noise. The behavior of the devised variable LL algorithm is verified and discussed using simulations and experiments. (C) 2022 Elsevier Inc. All rights reserved.