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Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation

In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. The...

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Autores principales: Dai, Zheng, Su, Weimin, Gu, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435974/
https://www.ncbi.nlm.nih.gov/pubmed/32751381
http://dx.doi.org/10.3390/s20154233
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author Dai, Zheng
Su, Weimin
Gu, Hong
author_facet Dai, Zheng
Su, Weimin
Gu, Hong
author_sort Dai, Zheng
collection PubMed
description In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm.
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spelling pubmed-74359742020-08-24 Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation Dai, Zheng Su, Weimin Gu, Hong Sensors (Basel) Letter In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm. MDPI 2020-07-29 /pmc/articles/PMC7435974/ /pubmed/32751381 http://dx.doi.org/10.3390/s20154233 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Dai, Zheng
Su, Weimin
Gu, Hong
Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
title Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
title_full Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
title_fullStr Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
title_full_unstemmed Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
title_short Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
title_sort gain-phase errors calibration for a linear array based on blind signal separation
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435974/
https://www.ncbi.nlm.nih.gov/pubmed/32751381
http://dx.doi.org/10.3390/s20154233
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