Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar

Abstract The phased‐array multiple‐input multiple‐output (PA‐MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space‐time adaptive processing (STAP) methods. Fortunately,...

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Autores principales:	Ning Cui, Kun Xing, Keqing Duan, Zhongjun Yu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/b7a3141149df48bd8f9f491160a7fef8
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spelling	oai:doaj.org-article:b7a3141149df48bd8f9f491160a7fef82021-11-12T15:34:30ZKnowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar1751-87921751-878410.1049/rsn2.12152https://doaj.org/article/b7a3141149df48bd8f9f491160a7fef82021-12-01T00:00:00Zhttps://doi.org/10.1049/rsn2.12152https://doaj.org/toc/1751-8784https://doaj.org/toc/1751-8792Abstract The phased‐array multiple‐input multiple‐output (PA‐MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space‐time adaptive processing (STAP) methods. Fortunately, the introduction of sparse recovery (SR) brings a novel perspective to reducing the sample requirement. However, most existing SR‐STAP methods only consider the clutter sparsity as prior information. In fact, more useful information, such as radar parameters, ground environment, airplane state, can all be obtained in advance and further utilised to improve the performance of SR‐STAP. In this study, the clutter structure, obtained based on the abovementioned prior knowledge, is utilised in the block SR‐STAP in the PA‐MIMO airborne radar. The clutter suppression performance is poor when the block SR algorithm is directly applied into the STAP because the clutter structure is destroyed by common vectorisation operation. To solve this problem, the vectorisation operation is first redesigned based on the clutter distribution characteristics and a novel dictionary is constructed. A basic block sparse Bayesian learning STAP scheme is then proposed in the PA‐MIMO radar’s application. The numerical experiments illustrate that the proposed method can achieve satisfactory performance with a few training samples and outperforms the state‐of‐the‐art SR‐STAP methods.Ning CuiKun XingKeqing DuanZhongjun YuWileyarticleTelecommunicationTK5101-6720ENIET Radar, Sonar & Navigation, Vol 15, Iss 12, Pp 1628-1642 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Ning Cui Kun Xing Keqing Duan Zhongjun Yu Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
description	Abstract The phased‐array multiple‐input multiple‐output (PA‐MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space‐time adaptive processing (STAP) methods. Fortunately, the introduction of sparse recovery (SR) brings a novel perspective to reducing the sample requirement. However, most existing SR‐STAP methods only consider the clutter sparsity as prior information. In fact, more useful information, such as radar parameters, ground environment, airplane state, can all be obtained in advance and further utilised to improve the performance of SR‐STAP. In this study, the clutter structure, obtained based on the abovementioned prior knowledge, is utilised in the block SR‐STAP in the PA‐MIMO airborne radar. The clutter suppression performance is poor when the block SR algorithm is directly applied into the STAP because the clutter structure is destroyed by common vectorisation operation. To solve this problem, the vectorisation operation is first redesigned based on the clutter distribution characteristics and a novel dictionary is constructed. A basic block sparse Bayesian learning STAP scheme is then proposed in the PA‐MIMO radar’s application. The numerical experiments illustrate that the proposed method can achieve satisfactory performance with a few training samples and outperforms the state‐of‐the‐art SR‐STAP methods.
format	article
author	Ning Cui Kun Xing Keqing Duan Zhongjun Yu
author_facet	Ning Cui Kun Xing Keqing Duan Zhongjun Yu
author_sort	Ning Cui
title	Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
title_short	Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
title_full	Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
title_fullStr	Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
title_full_unstemmed	Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar
title_sort	knowledge‐aided block sparse bayesian learning stap for phased‐array mimo airborne radar
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b7a3141149df48bd8f9f491160a7fef8
work_keys_str_mv	AT ningcui knowledgeaidedblocksparsebayesianlearningstapforphasedarraymimoairborneradar AT kunxing knowledgeaidedblocksparsebayesianlearningstapforphasedarraymimoairborneradar AT keqingduan knowledgeaidedblocksparsebayesianlearningstapforphasedarraymimoairborneradar AT zhongjunyu knowledgeaidedblocksparsebayesianlearningstapforphasedarraymimoairborneradar
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Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar

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