Recursive Constrained Adaptive Filtering Algorithm Based on Arctangent Framework

Abstract

In this brief, a robust constrained filtering algorithm is proposed by introducing a novel cost function framework into the constrained adaptive algorithm. The proposed algorithm is called the recursive constrained least arctangent (RCLA) adaptive algorithm. Thanks to the robustness of arctangent function, the proposed RCLA algorithm shows superior convergence performance and better steady-state behavior against impulsive noises compared to other existing recursive methods. The mean square convergence analysis and theoretical transient mean square deviation (MSD) are derived in detail. Besides, to validate the theoretical analysis, the computer simulations are conducted to demonstrate the consistency between theoretical and simulated MSD results. Simulation results under non-Gaussian environments verify the superior behavior of the proposed RCLA algorithm compared to known algorithms.