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      DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning

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          Abstract

          This study discusses the problem of direction-of-arrival estimation (DOA) estimation for a monostatic multiple-input multiple-output (MIMO) radar system, and a novel sparse Bayesian learning (SBL) framework is presented. To lower the computational load, the matched array data is firstly compressed via reduced-dimension transformation. Then the problem of DOA estimation is linked to a sparse inverse problem. Finally, a forgotten factor-based root SBL algorithm is derived from hyperparameters learning, which can solve the off-grid problem by finding the roots of a polynomial. The proposed algorithm does not require the prior of the source number, and it can apply to the scenario with a small snapshot as well as coarse grid, thus it has a blind and robust characteristic. Numerical simulations verify the effectiveness of the proposed algorithm.

          Most cited references24

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          MIMO Radar with Widely Separated Antennas

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            Sparse Bayesian Learning for Basis Selection

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              Sparse Bayesian learning and the releibvance vector machine

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                Author and article information

                Contributors
                Journal
                JOE
                The Journal of Engineering
                J. Eng.
                The Institution of Engineering and Technology
                2051-3305
                May 2018
                29 January 2018
                15 May 2018
                : 2018
                : 5
                : 268-273
                Affiliations
                [1 ] Electronic and Information School, Yangtze University , Jingzhou 434023, People's Republic of China
                [2 ] Information Department, Naval Command College , Nanjing 210016, People's Republic of China
                Article
                JOE.2017.0872 JOE.2017.0872
                10.1049/joe.2017.0872
                678a3625-aa88-4f77-bc03-3032441f5cb2

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                History
                : 17 December 2017
                : 25 January 2018
                Funding
                Funded by: China NSF
                Award ID: 61471191
                Award ID: 61501233
                Award ID: 61701046
                Categories
                ee-sip

                Software engineering,Data structures & Algorithms,Robotics,Networking & Internet architecture,Artificial intelligence,Human-computer-interaction
                multiple-output radar system,(SBL) framework,Bayes methods,inverse problems,DOA estimation,novel sparse Bayesian,hyperparameters learning,computational load,MIMO radar,root SBL algorithm,learning (artificial intelligence),enhanced sparse Bayesian learning,direction-of-arrival estimation estimation,reduced-dimension transformation,direction-of-arrival estimation,sparse inverse problem,off-grid problem,monostatic MIMO radar,matched array data

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