Filter proportionate normalized least mean square algorithm for a sparse system

Rosalin; Nirmal Kumar Rout; Debi Prasad Das

doi:10.1002/acs.3058

journal article Oct 24, 2019

Filter proportionate normalized least mean square algorithm for a sparse system

Rosalin Nirmal Kumar Rout Debi Prasad Das

International Journal of Adaptive Control and Signal Processing Vol. 33 No. 11 pp. 1695-1705 · Wiley

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Abstract

SummaryIn 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.

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References

Details

Published: Oct 24, 2019
Vol/Issue: 33(11)
Pages: 1695-1705
License: View

Authors

R

Rosalin

School of Electronics Engineering KIIT University Bhubaneswar India

N

Nirmal Kumar Rout

School of Electronics Engineering KIIT University Bhubaneswar India

D

Debi Prasad Das

Process Modelling and Instrumentation Department CSIR‐Institute of Minerals and Materials Technology Bhubaneswar India

Cite This Article

Rosalin, Nirmal Kumar Rout, Debi Prasad Das (2019). Filter proportionate normalized least mean square algorithm for a sparse system. International Journal of Adaptive Control and Signal Processing, 33(11), 1695-1705. https://doi.org/10.1002/acs.3058

Filter proportionate normalized least mean square algorithm for a sparse system

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