Affine projection LMS adaptive algorithm with variable smoothing of weight update matrix

Improving the convergence speed of adaptive filters is crucial for enhancing performance inapplications involving highly correlated input signals. In this paper, we propose a novel method to improvethe convergence performance of the affine projection LMS (AP-LMS) algorithm by incorporating a variablesmoothing approach for the weight update matrix. The smoothing parameter is dynamically assignedbased on the difference between the instantaneous and smoothed values of the weight update matrix.Simulation results for FIR system modeling demonstrate that the proposed algorithm achieves superiorconvergence performance in estimating system coefficients compared to competing adaptive algorithms for both stationary and non-stationary input signals.