A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar

Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm. The unique nonstationary characteristic of signal for space-based early warning radar (SBEWR) leads to the spatial variation of trainin...

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Autores principales:	Tianfu Zhang, Zhihao Wang, Ning Qiao, Shuangxi Zhang, Mengdao Xing, Yongliang Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Covariance matrix estimation heterogeneous environment space-based early warning radar (SBEWR) spatial-temporal adaptive processing (STAP) training sample Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/00ca714274e046a6b59cc3c8c95aa4be
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id	oai:doaj.org-article:00ca714274e046a6b59cc3c8c95aa4be
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spelling	oai:doaj.org-article:00ca714274e046a6b59cc3c8c95aa4be2021-11-17T00:00:14ZA Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar2151-153510.1109/JSTARS.2021.3123648https://doaj.org/article/00ca714274e046a6b59cc3c8c95aa4be2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594675/https://doaj.org/toc/2151-1535Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm. The unique nonstationary characteristic of signal for space-based early warning radar (SBEWR) leads to the spatial variation of training sample and the insufficient number of optional independent identically distributed (i.i.d.) training samples, which brings difficulties to training sample selection and covariance matrix estimation. To improve the estimation accuracy of clutter covariance matrix and the performance of STAP for SBEWR in a heterogeneous environment, a novel training sample selection and clutter covariance matrix estimation method is proposed. The method based on clutter subspace reconstruction and spectrum correction technology can improve the estimation accuracy of clutter covariance matrix in the case of nonstationary signals and heterogeneous environments. The clutter covariance matrix estimated by the proposed method is similar to the clutter covariance matrix of the CUT, and the performance of STAP is improved. The experimental results confirm the performance of the proposed method.Tianfu ZhangZhihao WangNing QiaoShuangxi ZhangMengdao XingYongliang WangIEEEarticleCovariance matrix estimationheterogeneous environmentspace-based early warning radar (SBEWR)spatial-temporal adaptive processing (STAP)training sampleOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11217-11228 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Covariance matrix estimation heterogeneous environment space-based early warning radar (SBEWR) spatial-temporal adaptive processing (STAP) training sample Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Covariance matrix estimation heterogeneous environment space-based early warning radar (SBEWR) spatial-temporal adaptive processing (STAP) training sample Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Tianfu Zhang Zhihao Wang Ning Qiao Shuangxi Zhang Mengdao Xing Yongliang Wang A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
description	Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm. The unique nonstationary characteristic of signal for space-based early warning radar (SBEWR) leads to the spatial variation of training sample and the insufficient number of optional independent identically distributed (i.i.d.) training samples, which brings difficulties to training sample selection and covariance matrix estimation. To improve the estimation accuracy of clutter covariance matrix and the performance of STAP for SBEWR in a heterogeneous environment, a novel training sample selection and clutter covariance matrix estimation method is proposed. The method based on clutter subspace reconstruction and spectrum correction technology can improve the estimation accuracy of clutter covariance matrix in the case of nonstationary signals and heterogeneous environments. The clutter covariance matrix estimated by the proposed method is similar to the clutter covariance matrix of the CUT, and the performance of STAP is improved. The experimental results confirm the performance of the proposed method.
format	article
author	Tianfu Zhang Zhihao Wang Ning Qiao Shuangxi Zhang Mengdao Xing Yongliang Wang
author_facet	Tianfu Zhang Zhihao Wang Ning Qiao Shuangxi Zhang Mengdao Xing Yongliang Wang
author_sort	Tianfu Zhang
title	A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
title_short	A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
title_full	A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
title_fullStr	A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
title_full_unstemmed	A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar
title_sort	novel clutter covariance matrix estimation method based on feature subspace for space-based early warning radar
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/00ca714274e046a6b59cc3c8c95aa4be
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A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar

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