A fast-implemented recursive inverse adaptive filtering algorithm

Abstract

The recently proposed Recursive Inverse (RI) adaptive algorithm has shown improved performance in channel equalization and system identification settings. Although its computational complexity is lower than those of the RLS and Robust RLS algorithms, its computational complexity can be reduced further. A fast implementation method is applied in this paper to decrease its computational complexity. The performance of the fast implemented RI algorithm is compared to those of the Variable Step-Size LMS (VSSLMS), Discrete Cosine Transform LMS (DCTLMS) and Recursive-Least-Squares (RLS) algorithms in Additive White Gaussian Noise (AWGN), Additive Correlated Gaussian Noise (ACGN), Additive White Impulsive Noise (AWIN) and Additive Correlated Impulsive Noise (ACIN) environments in a noise cancellation setting. Simulation results show that the Fast RI algorithm performs better than the VSSLMS and DCTLMS algorithms. Its performance is the same as in the RLS algorithm with a considerable reduction in complexity.