A MSWF root‐MUSIC based on Pseudo‐noise resampling technique

Abstract This paper uses the shift‐invariance property of uniform linear array in root‐MUSIC estimator for obtaining signal and noise subspaces by applying multistage Wiener filter (MSWF) procedure. Also, the MSWF root‐MUSIC based on the pseudo‐noise resampling process for estimating the direction o...

Full description

Bibliographic Details
Main Authors: M. Johnny, M. R. Aref
Format: Article
Language:English
Published: Wiley 2021-08-01
Series:Electronics Letters
Online Access:https://doi.org/10.1049/ell2.12220
id doaj-72534487e68d4a9fb3aaf9b29953e7ae
record_format Article
spelling doaj-72534487e68d4a9fb3aaf9b29953e7ae2021-08-11T03:06:51ZengWileyElectronics Letters0013-51941350-911X2021-08-01571767567810.1049/ell2.12220A MSWF root‐MUSIC based on Pseudo‐noise resampling techniqueM. Johnny0M. R. Aref1Department of electrical Engineering Islamic Azad University, Science and Research Branch Tehran IranDepartment of electrical Engineering Sharif University of Technology Tehran IranAbstract This paper uses the shift‐invariance property of uniform linear array in root‐MUSIC estimator for obtaining signal and noise subspaces by applying multistage Wiener filter (MSWF) procedure. Also, the MSWF root‐MUSIC based on the pseudo‐noise resampling process for estimating the direction of arrival (DOA) of signals is proposed. By this process, a root estimator bank and a corresponding DOA estimator bank are constructed. Then, a hypothesis test is applied to the DOA estimator bank to detect the normal DOA estimators from abnormal DOA estimators called outliers. By averaging the corresponding root estimators of normal DOA estimators, the final DOAs can be determined more accurately. When all the DOA estimators fail to pass the hypothesis test, the criterion based on the Gaussian weight average of the root estimator bank is introduced. By applying this criterion, better outlier‐free performance of MSWF root‐MUSIC can be obtained. Simulations show that our method can improve the DOA estimations, especially in small sample sizes and low signal‐to‐noise ratios.https://doi.org/10.1049/ell2.12220
collection DOAJ
language English
format Article
sources DOAJ
author M. Johnny
M. R. Aref
spellingShingle M. Johnny
M. R. Aref
A MSWF root‐MUSIC based on Pseudo‐noise resampling technique
Electronics Letters
author_facet M. Johnny
M. R. Aref
author_sort M. Johnny
title A MSWF root‐MUSIC based on Pseudo‐noise resampling technique
title_short A MSWF root‐MUSIC based on Pseudo‐noise resampling technique
title_full A MSWF root‐MUSIC based on Pseudo‐noise resampling technique
title_fullStr A MSWF root‐MUSIC based on Pseudo‐noise resampling technique
title_full_unstemmed A MSWF root‐MUSIC based on Pseudo‐noise resampling technique
title_sort mswf root‐music based on pseudo‐noise resampling technique
publisher Wiley
series Electronics Letters
issn 0013-5194
1350-911X
publishDate 2021-08-01
description Abstract This paper uses the shift‐invariance property of uniform linear array in root‐MUSIC estimator for obtaining signal and noise subspaces by applying multistage Wiener filter (MSWF) procedure. Also, the MSWF root‐MUSIC based on the pseudo‐noise resampling process for estimating the direction of arrival (DOA) of signals is proposed. By this process, a root estimator bank and a corresponding DOA estimator bank are constructed. Then, a hypothesis test is applied to the DOA estimator bank to detect the normal DOA estimators from abnormal DOA estimators called outliers. By averaging the corresponding root estimators of normal DOA estimators, the final DOAs can be determined more accurately. When all the DOA estimators fail to pass the hypothesis test, the criterion based on the Gaussian weight average of the root estimator bank is introduced. By applying this criterion, better outlier‐free performance of MSWF root‐MUSIC can be obtained. Simulations show that our method can improve the DOA estimations, especially in small sample sizes and low signal‐to‐noise ratios.
url https://doi.org/10.1049/ell2.12220
work_keys_str_mv AT mjohnny amswfrootmusicbasedonpseudonoiseresamplingtechnique
AT mraref amswfrootmusicbasedonpseudonoiseresamplingtechnique
AT mjohnny mswfrootmusicbasedonpseudonoiseresamplingtechnique
AT mraref mswfrootmusicbasedonpseudonoiseresamplingtechnique
_version_ 1721211712765427712