Robust Total Maximum Versoria Algorithm for Efficient DOA Estimation in Noisy Inputs

In adaptive filtering for direction-of-arrival (DOA) estimation, traditional bias-compensated models often suffer from significant performance degradation in the presence of impulsive noise affecting both input and output signals. This paper addresses this challenge by introducing new algorithms for robust DOA estimation tailored for environments contaminated by impulsive noise. We initially employ the error-in-variable (EIV) model to derive a generalized framework for robust adaptive DOA estimation algorithms. Our methodology incorporates the Maximum Versoria (MV) criterion as a cost function, facilitating the development of the robust Gradient Total MV (GTMV) algorithm. To further enhance performance, we introduce a variable step-size mechanism within the GTMV framework (VSS-GTMV), which dynamically adjusts the step size based on the signal-to-noise ratio (SNR) to optimize the trade-off between convergence speed and steady-state error. The convergence properties of the GTMV algorithm are analytically examined, and its effectiveness is substantiated through extensive numerical simulations in impulsive noise environments. Comparative results demonstrate the superiority of our proposed methods over existing competitive approaches in terms of accuracy and robustness.