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Multiresolution Transform based Denoising in Direction Finding

Published on September 2017 by K. Gowri, P. Palnisamy
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International Conference on Microelectronics, Circuits and System
Foundation of Computer Science USA
MICRO2016 - Number 1
September 2017
Authors: K. Gowri, P. Palnisamy
a5c2a63e-cb34-4d23-8d41-8543bbfe440e

K. Gowri, P. Palnisamy . Multiresolution Transform based Denoising in Direction Finding. International Conference on Microelectronics, Circuits and System. MICRO2016, 1 (September 2017), 31-38.

@article{
author = { K. Gowri, P. Palnisamy },
title = { Multiresolution Transform based Denoising in Direction Finding },
journal = { International Conference on Microelectronics, Circuits and System },
issue_date = { September 2017 },
volume = { MICRO2016 },
number = { 1 },
month = { September },
year = { 2017 },
issn = 0975-8887,
pages = { 31-38 },
numpages = 8,
url = { /proceedings/micro2016/number1/28440-6108/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Microelectronics, Circuits and System
%A K. Gowri
%A P. Palnisamy
%T Multiresolution Transform based Denoising in Direction Finding
%J International Conference on Microelectronics, Circuits and System
%@ 0975-8887
%V MICRO2016
%N 1
%P 31-38
%D 2017
%I International Journal of Computer Applications
Abstract

In this paper, multi-resolution transforms based denoising followed by an improved method of Direction of Arrival (DOA) estimation is investigated. The predominant subspace method, Multiple Signal Classification (MUSIC) algorithm is very practical and efficient for direction of arrival estimation, but it fails to determine the direction at low Signal to Noise Ratio (SNR). The pre-eminence of MUSIC algorithm is used to upgrade the resolution of direction of arrival under adverse noisy situations. The noise is suppressed and thereby the gain of the received signal from sensors is improved by ridgelet transform and GHM (Geronimo J. S, Hardin D. P and Massopust P. R) multiwavelet transform based denoising. The simulation results of denoising and pseudo spectrum of the algorithm delivers improved performance in terms of root mean square error (RMSE), spectrum function, bias and gain. SNR, snapshots, array elements are the input parameters.

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Index Terms

Computer Science
Information Sciences

Keywords

Multiresolution Transform Ridgelet Transform Ghm Multiwavelet Transform Doa Estimation Denoising Subspace Methods