Filter proportionate normalized least mean square algorithm for a sparse system. (24th October 2019)

• Record Type: Journal Article
• Authors: Rosalin, ; Rout, Nirmal Kumar; Das, Debi Prasad
• Is Part Of: International journal of adaptive control and signal processing. Volume 33:Number 11(2019)
• Page Start: 1695
• Page End: 1705
• Publication Date: 2019-10-24
• Subjects: compression -- proportionate -- sparse; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3058
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In this paper, the proportionate normalized least mean square (PNLMS) and its modifications, such as improved PNLMS (IPNLMS) and μ‐law PNLMS (MPNLMS) algorithms, developed for a sparse system, are analyzed for a compressed input signal. This analysis is based on a comparative study of the steady‐state error and convergence time for the original signal and the compressed signal. Further, in this paper, a filter PNLMS (FPNLMS) algorithm that is a modification of the IPNLMS algorithm is proposed. The FPNLMS algorithm uses a step size varying in time to adapt to the sparse system. Simulations are carried out to compare the proposed FPNLMS algorithm for different signal‐to‐noise ratio for a compressed input signal with existing algorithms, ie, PNLMS, MPNLMS, and IPNLMS algorithms. The FPNLMS algorithm achieves a better steady‐state and convergence time compared with other existing algorithms in both low and high SNRs. The FPNLMS algorithm is further simulated for a real transfer function to show its robustness compared with existing algorithms.