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A unified analysis of proposed wavelet transform domain LMS-algorithm for ARMA process

conference contribution
posted on 14.05.2020, 00:00 by S Rahman, MD Mamunur Rashid, MZ Alam
A unified analysis of Wavelet Transform (WT) domain Least Mean Square (LMS) adaptive filter is presented in this work for highly correlated Autoregressive-Moving-Average (ARMA) input process. It is well known that the Unitary transform (UT) domain LMS (UT-LMS) adaptive filter for Autoregressive (AR) process with power normalization improves the filter performance, where DCT provides best performance among them. In this work, we apply the UT-LMS algorithm for time-varying ARMA process, and the analytical result shows that the lower decorrelation property of UT degrades the LMS performance. As a result, Unitary transform is not applicable for LMS as a transform algorithm for ARMA process, and this outcome has not been explored in early published work. In this paper, we propose Discrete Wavelet domain LMS (DWT-LMS) for ARMA process to enhance the basic performances of LMS such as misadjustment, convergence, and tracking properties, and the theoretical and simulation result of this work show that DWT-LMS provides better performance than that of DCT-LMS for 1st and 2n d order AR, Moving-average (MA), and ARMA process. This paper concludes with the MATLAB simulation for the proposed method with various inputs for demonstrating the validity of the derived mathematical algorithm. © 2019 IEEE.

History

Start Page

195

End Page

200

Number of Pages

6

Start Date

26/09/2019

Finish Date

28/09/2019

eISSN

2378-2692

ISSN

2378-2668

ISBN-13

9781728149349

Location

Dhaka, Bangladesh

Publisher

ICAEE

Place of Publication

Dhaka, Bangladesh

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

The University of Sydney; Rajshahi University, Bangladesh

Era Eligible

Yes

Name of Conference

proceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE)

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