Adaptive Radar Detection in Gaussian Disturbance With Structured Covariance Matrix via Invariance Theory
This paper deals with adaptive radar detection of targets in the presence of Gaussian disturbance sharing a block-diagonal covariance structure. The problem is formulated according to a very general signal model, which contains the point-like, range-spread, and subspace target (or targets) as specia...
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| Veröffentlicht in: | IEEE transactions on signal processing 2019-11, Vol.67 (21), p.5671-5685 |
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| container_title | IEEE transactions on signal processing |
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| creator | Tang, Mengjiao Rong, Yao De Maio, Antonio Chen, Chen Zhou, Jie |
| description | This paper deals with adaptive radar detection of targets in the presence of Gaussian disturbance sharing a block-diagonal covariance structure. The problem is formulated according to a very general signal model, which contains the point-like, range-spread, and subspace target (or targets) as special instances. Hence, a unified study on the resulting adaptive detection problem is handled with the use of the invariance theory. The obtained results, including an appropriate transformation group, a maximal invariant and an induced maximal invariant, are proven consistent with those existing in the literature for some simple scenarios. Meanwhile, since the widely-used generalized likelihood ratio detector does not admit a closed form expression, new invariant detectors and their CFAR versions are proposed in this general scenario. Finally, their detection performance is assessed and validated via numerical examples. |
| doi_str_mv | 10.1109/TSP.2019.2941119 |
| format | Article |
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The problem is formulated according to a very general signal model, which contains the point-like, range-spread, and subspace target (or targets) as special instances. Hence, a unified study on the resulting adaptive detection problem is handled with the use of the invariance theory. The obtained results, including an appropriate transformation group, a maximal invariant and an induced maximal invariant, are proven consistent with those existing in the literature for some simple scenarios. Meanwhile, since the widely-used generalized likelihood ratio detector does not admit a closed form expression, new invariant detectors and their CFAR versions are proposed in this general scenario. 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Finally, their detection performance is assessed and validated via numerical examples.</description><subject>Adaptive detection</subject><subject>block-diagonal covariance</subject><subject>Closed-form solutions</subject><subject>Covariance matrices</subject><subject>Covariance matrix</subject><subject>Detectors</subject><subject>Gaussian noise</subject><subject>Interference</subject><subject>Invariance</subject><subject>invariant detectors</subject><subject>Invariants</subject><subject>Likelihood ratio</subject><subject>Matrix decomposition</subject><subject>maximal invariant</subject><subject>Radar detection</subject><subject>statistical invariance</subject><subject>Target detection</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLAzEUhYMoWKt7wU3A9dSbxzyyLK3WgqLYiu7CnZmUpuhMTTLF_ntTW7q6h3PPffARcs1gwBiou_nsdcCBqQFXkjGmTkiPRZWAzLPTqCEVSVrkn-fkwvsVAJNSZT2yHNa4DnZj6BvW6OjYBFMF2zbUNnSCnfcWGzq2PnSuxKYy9MOGJZ0F11XRMjUdtRt09r_1jMHZX7qxSKfN0Z0vTeu2l-RsgV_eXB1qn7w_3M9Hj8nTy2Q6Gj4lFVcsJCIteY0iRYCSlwAV1LyUClMjpeQlE6JeFFm6YFV8HwsRfSVMhQaNkJnKRZ_c7veuXfvTGR_0qu1cE09qLiAXwArFYwr2qcq13juz0Gtnv9FtNQO946kjT73jqQ8848jNfsQaY47xohCyyED8Aaudcfo</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Tang, Mengjiao</creator><creator>Rong, Yao</creator><creator>De Maio, Antonio</creator><creator>Chen, Chen</creator><creator>Zhou, Jie</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The problem is formulated according to a very general signal model, which contains the point-like, range-spread, and subspace target (or targets) as special instances. Hence, a unified study on the resulting adaptive detection problem is handled with the use of the invariance theory. The obtained results, including an appropriate transformation group, a maximal invariant and an induced maximal invariant, are proven consistent with those existing in the literature for some simple scenarios. Meanwhile, since the widely-used generalized likelihood ratio detector does not admit a closed form expression, new invariant detectors and their CFAR versions are proposed in this general scenario. Finally, their detection performance is assessed and validated via numerical examples.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSP.2019.2941119</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-8421-3318</orcidid><orcidid>https://orcid.org/0000-0002-6203-3583</orcidid><orcidid>https://orcid.org/0000-0003-1654-8428</orcidid><orcidid>https://orcid.org/0000-0002-3743-713X</orcidid></addata></record> |
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| subjects | Adaptive detection block-diagonal covariance Closed-form solutions Covariance matrices Covariance matrix Detectors Gaussian noise Interference Invariance invariant detectors Invariants Likelihood ratio Matrix decomposition maximal invariant Radar detection statistical invariance Target detection |
| title | Adaptive Radar Detection in Gaussian Disturbance With Structured Covariance Matrix via Invariance Theory |
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