A Fast Convergence and Robust Algorithm for Adaptive IIR Noteh Filter

摘要

This paper aims to improved the convergence rate, noise robustness and less computational complexity. The proposed algorithm is for a second order adaptive IIR notch filter. It is established based on a new memoryless nonlinear gradient algorithm and a least mean p-power error criterion with a value of p equal to unity. The proposed algorithm is very attractive due to their computational efficiencies and improved convergence properties. It is revealed by extensive simulations that it can produce significantly improved frequency estimates in both Gaussian and impulsive noise scenarios compared with the new memoryless nonlinear gradient algorithm and the other existing gradient-type algorithms. Several simulated results are provided to show the superiority of the proposed algorithm.