Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture
In this study, the authors consider the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure, but different power levels (textures). A Bayesian framework is exploited where bo...
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| Published in | IET radar, sonar & navigation Vol. 10; no. 4; pp. 689 - 698 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
The Institution of Engineering and Technology
01.04.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1751-8784 1751-8792 |
| DOI | 10.1049/iet-rsn.2015.0241 |
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| Abstract | In this study, the authors consider the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure, but different power levels (textures). A Bayesian framework is exploited where both the textures and the structure are assumed to be random. Precisely, the textures follow Gamma distribution or inverse Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, two generalised likelihood ratio tests are derived. Finally, they evaluate the capabilities of the proposed detectors against compound-Gaussian clutter as well as their superiority with respect to some existing techniques. |
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| AbstractList | In this study, the authors consider the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of the primary and the secondary data share a common structure, but different power levels (textures). A Bayesian framework is exploited where both the textures and the structure are assumed to be random. Precisely, the textures follow Gamma distribution or inverse Gamma distribution and the structure is drawn from an inverse complex Wishart distribution. In this framework, two generalised likelihood ratio tests are derived. Finally, they evaluate the capabilities of the proposed detectors against compound-Gaussian clutter as well as their superiority with respect to some existing techniques. |
| Author | Li, Na Liu, Qing Huo Kong, Lingjiang Cui, Guolong Yang, Haining |
| Author_xml | – sequence: 1 givenname: Lingjiang surname: Kong fullname: Kong, Lingjiang organization: 1Department of Signal and Information Processing, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China – sequence: 2 givenname: Na surname: Li fullname: Li, Na organization: 1Department of Signal and Information Processing, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China – sequence: 3 givenname: Guolong surname: Cui fullname: Cui, Guolong email: cuiguolong@uestc.edu.cn organization: 1Department of Signal and Information Processing, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China – sequence: 4 givenname: Haining surname: Yang fullname: Yang, Haining organization: 1Department of Signal and Information Processing, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China – sequence: 5 givenname: Qing Huo surname: Liu fullname: Liu, Qing Huo organization: 2Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA |
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| CitedBy_id | crossref_primary_10_1016_j_sigpro_2017_08_023 crossref_primary_10_1109_JSEN_2024_3492137 crossref_primary_10_1007_s11432_016_9105_7 crossref_primary_10_1587_transcom_2017EBP3418 crossref_primary_10_1109_TAES_2022_3188228 crossref_primary_10_1049_rsn2_12196 crossref_primary_10_1016_j_sigpro_2019_03_008 crossref_primary_10_1016_j_dsp_2019_102590 crossref_primary_10_1109_LGRS_2022_3140727 crossref_primary_10_1016_j_sigpro_2023_109256 crossref_primary_10_1109_TSP_2018_2879038 crossref_primary_10_1007_s00034_018_0783_y crossref_primary_10_3390_math11030680 crossref_primary_10_1016_j_sigpro_2018_02_019 |
| Cites_doi | 10.1109/7.249117 10.1109/TAES.2006.248194 10.1109/MSP.2006.1593332 10.1109/TAES.2010.5545189 10.1049/ip-rsn:19971107 10.1109/TAES.2003.1188909 10.1109/TSP.2010.2052922 10.1109/JSTSP.2009.2038980 10.1109/TSP.2007.901652 10.1109/LSP.2006.888088 10.1109/TSP.2002.800412 10.1109/TAES.2012.6129621 10.1109/MSP.2008.4408448 10.1007/s00034-013-9718-9 10.1049/iet-rsn.2011.0376 10.1109/97.511809 10.1109/TSP.2007.901664 10.1109/TAES.2006.248215 10.1109/TAES.2007.4285370 10.1109/LSP.2013.2255272 10.1109/TAES.2004.1337463 10.1109/TAES.2010.5417154 10.1109/19.650784 10.1109/TSP.2007.908995 10.1016/S0165-1684(02)00315-8 10.1109/TAES.2007.4383581 10.1016/S0165-1684(99)00135-8 |
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| Keywords | radar detection inverse complex Wishart distribution radar clutter MIMO radar gamma distribution multiple-input multiple-output radar adaptive Bayesian detection Bayes methods compound-Gaussian clutter random texture generalised likelihood ratio tests inverse Gamma distribution |
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Signal Process. – volume: 48 start-page: 64 issue: 1 year: 2012 end-page: 77 article-title: Coherent radar target detection in heavy‐tailed compound‐Gaussian clutter publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 46 start-page: 836 issue: 4 year: 1997 end-page: 841 article-title: A procedure for measuring the coherence length of the sea texture publication-title: IEEE Trans. Instrum. Meas. – volume: 25 start-page: 116 issue: 1 year: 2008 end-page: 129 article-title: MIMO radar with widely separated antennas publication-title: IEEE Signal Process. Mag. – volume: 4 start-page: 115 issue: 1 year: 2010 end-page: 126 article-title: MIMO radar detection in non‐Gaussian and heterogeneous clutter publication-title: IEEE J. Sel. Top. Signal Process. – year: 1998 – volume: 33 start-page: 1819 issue: 6 year: 2014 end-page: 1839 article-title: MIMO radar moving target detection against compound‐Gaussian clutter publication-title: Circuits Syst. Signal Process. – volume: 137 start-page: 51 issue: 2 year: 1990 end-page: 62 article-title: Maritime surveillance radar. Part 1: Radar scattering from the ocean surface publication-title: IEE Proc. F – volume: 144 start-page: 121 issue: 3 year: 1997 end-page: 130 article-title: High resolution sea clutter data: Statistical analysis of recorded live data publication-title: IEE Proc., Radar Sonar Navig. – volume: 138 start-page: 121 issue: 2 year: 1991 end-page: 130 article-title: Modelling and simulation of non‐Rayleigh radar clutter publication-title: IEE Proc. F – volume: 42 start-page: 983 issue: 3 year: 2006 end-page: 1120 article-title: Special section on knowledge‐aided sensor signal and data processing publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 82 start-page: 1847 issue: 12 year: 2002 end-page: 1859 article-title: Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter publication-title: Signal Process. – volume: 46 start-page: 1290 issue: 3 year: 2010 end-page: 1301 article-title: MIMO radar moving target detection in homogeneous clutter publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 58 start-page: 5390 issue: 10 year: 2010 end-page: 5396 article-title: Knowledge‐aided covariance matrix estimation and adaptive detection in compound‐Gaussian noise publication-title: IEEE Trans. Signal Process. – volume: 50 start-page: 1908 issue: 8 year: 2002 end-page: 1915 article-title: Recursive estimation of the covariance matrix of a compound‐Gaussian process and its application to adaptive CFAR detection publication-title: IEEE Trans. Signal Process. – volume: 80 start-page: 365 issue: 2 year: 2000 end-page: 371 article-title: Performance analysis of two structured covariance matrix estimators in compound‐Gaussian clutter publication-title: Signal Process. – volume: 46 start-page: 170 issue: 1 year: 2010 end-page: 183 article-title: Knowledge‐aided Bayesian radar detectors and their application to live data publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 56 start-page: 205 issue: 1 year: 2008 end-page: 217 article-title: A Bayesian approach to adaptive detection in non‐homogeneous environments publication-title: IEEE Trans. Signal Process. – volume: 29 start-page: 111 issue: 1 year: 1993 end-page: 124 article-title: Non‐Gaussian vector identification using spherically invariant random processes publication-title: IEEE Trans. Aerosp. Electron. Syst. – volume: 43 start-page: 886 issue: 3 year: 2007 end-page: 898 article-title: Design principles of MIMO radar detectors publication-title: IEEE Trans. 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| Snippet | In this study, the authors consider the adaptive detection with multiple-input multiple-output radar in compound-Gaussian clutter. The covariance matrices of... In this study, the authors consider the adaptive detection with multiple‐input multiple‐output radar in compound‐Gaussian clutter. The covariance matrices of... |
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| SubjectTerms | adaptive Bayesian detection Bayes methods Bayesian analysis Clutter compound‐Gaussian clutter gamma distribution generalised likelihood ratio tests Inverse inverse complex Wishart distribution inverse Gamma distribution MIMO radar multiple‐input multiple‐output radar Navigation Probability distribution functions Radar radar clutter radar detection random texture Surface layer Texture |
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| Title | Adaptive Bayesian detection for multiple-input multiple-output radar in compound-Gaussian clutter with random texture |
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