New Low-Rank Filters for MIMO-STAP Based on an Orthogonal Tensorial Decomposition
We develop in this paper a new adaptive low-rank (LR) filter for MIMO-space time adaptive processing (STAP) application based on a tensorial modeling of the data. This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of si...
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| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 2018-06, Vol.54 (3), p.1208-1220 |
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| container_title | IEEE transactions on aerospace and electronic systems |
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| creator | Brigui, Frederic Boizard, Maxime Ginolhac, Guillaume Pascal, Frederic |
| description | We develop in this paper a new adaptive low-rank (LR) filter for MIMO-space time adaptive processing (STAP) application based on a tensorial modeling of the data. This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of singular value decomposition to the tensor case), called alternative unfolding HOSVD (AU-HOSVD), which allows us to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modeling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. Results on simulated data show the good performance of the AU-HOSVD LR filters in terms of secondary data and clutter notch. |
| doi_str_mv | 10.1109/TAES.2017.2776679 |
| format | Article |
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This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of singular value decomposition to the tensor case), called alternative unfolding HOSVD (AU-HOSVD), which allows us to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modeling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. 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This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of singular value decomposition to the tensor case), called alternative unfolding HOSVD (AU-HOSVD), which allows us to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modeling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. Results on simulated data show the good performance of the AU-HOSVD LR filters in terms of secondary data and clutter notch.</description><subject>Clutter</subject><subject>Covariance matrices</subject><subject>Engineering Sciences</subject><subject>Low-rank (LR) clutter</subject><subject>MIMO</subject><subject>MIMO communication</subject><subject>MIMO radar</subject><subject>orthogonal tensor decomposition</subject><subject>radar</subject><subject>Signal and Image processing</subject><subject>Singular value decomposition</subject><subject>space time adaptive processing (STAP)</subject><subject>Tensors</subject><issn>0018-9251</issn><issn>1557-9603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwAYiNtyxS_EjseBlKSyulFGhYW05i00AaIzui4u9x1KqreejemasDwC1GE4yReCiy2WZCEOYTwjljXJyBEU4SHgmG6DkYIYTTSJAEX4Ir77_CGKcxHYG3F72Hud1H76r7hvOm7bXz0FgHV8vVOtoU2St8VF7X0HZQdXDt-q39tJ1qYaE7b10Tuidd2d2P9U3f2O4aXBjVen1zrGPwMZ8V00WUr5-X0yyPKhqTPoq5KlFah1iYYWVEWdbaCGJSyghH1FBSq5JqUyWIs4TFDKEECY10mlQYh-xjcH-4u1Wt_HHNTrk_aVUjF1kuh12AkYpg_sVBiw_aylnvnTYnA0Zy4CcHfnLgJ4_8gufu4Gm01id9Smj4jug_NJBptQ</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Brigui, Frederic</creator><creator>Boizard, Maxime</creator><creator>Ginolhac, Guillaume</creator><creator>Pascal, Frederic</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-9318-028X</orcidid><orcidid>https://orcid.org/0000-0003-0196-6395</orcidid></search><sort><creationdate>20180601</creationdate><title>New Low-Rank Filters for MIMO-STAP Based on an Orthogonal Tensorial Decomposition</title><author>Brigui, Frederic ; Boizard, Maxime ; Ginolhac, Guillaume ; Pascal, Frederic</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c342t-47ab08d018161af9bbdef92f8362703f32dab3efc5076564600509e0e85c11843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Clutter</topic><topic>Covariance matrices</topic><topic>Engineering Sciences</topic><topic>Low-rank (LR) clutter</topic><topic>MIMO</topic><topic>MIMO communication</topic><topic>MIMO radar</topic><topic>orthogonal tensor decomposition</topic><topic>radar</topic><topic>Signal and Image processing</topic><topic>Singular value decomposition</topic><topic>space time adaptive processing (STAP)</topic><topic>Tensors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brigui, Frederic</creatorcontrib><creatorcontrib>Boizard, Maxime</creatorcontrib><creatorcontrib>Ginolhac, Guillaume</creatorcontrib><creatorcontrib>Pascal, Frederic</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>IEEE transactions on aerospace and electronic systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Brigui, Frederic</au><au>Boizard, Maxime</au><au>Ginolhac, Guillaume</au><au>Pascal, Frederic</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New Low-Rank Filters for MIMO-STAP Based on an Orthogonal Tensorial Decomposition</atitle><jtitle>IEEE transactions on aerospace and electronic systems</jtitle><stitle>T-AES</stitle><date>2018-06-01</date><risdate>2018</risdate><volume>54</volume><issue>3</issue><spage>1208</spage><epage>1220</epage><pages>1208-1220</pages><issn>0018-9251</issn><eissn>1557-9603</eissn><coden>IEARAX</coden><abstract>We develop in this paper a new adaptive low-rank (LR) filter for MIMO-space time adaptive processing (STAP) application based on a tensorial modeling of the data. This filter is based on an extension of the higher order singular value decomposition (HOSVD) (which is also one possible extension of singular value decomposition to the tensor case), called alternative unfolding HOSVD (AU-HOSVD), which allows us to consider the combinations of dimensions. This property is necessary to keep the advantages of the STAP and the MIMO characteristics of the data. We show that the choice of a good partition (as well as the tensorial modeling) is not heuristic but have to follow several features. Thanks to the derivation of the theoretical formulation of multimode ranks for all partitions, the tensorial LR filters are easy to compute. 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| subjects | Clutter Covariance matrices Engineering Sciences Low-rank (LR) clutter MIMO MIMO communication MIMO radar orthogonal tensor decomposition radar Signal and Image processing Singular value decomposition space time adaptive processing (STAP) Tensors |
| title | New Low-Rank Filters for MIMO-STAP Based on an Orthogonal Tensorial Decomposition |
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