DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning
- Author(s): Fangqing Wen 1 ; Dongmei Huang 2 ; Ke Wang 1 ; Lei Zhang 1
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View affiliations
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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
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Affiliations:
1:
Electronic and Information School, Yangtze University , Jingzhou 434023 , People's Republic of China ;
- Source:
Volume 2018, Issue 5,
May
2018,
p.
268 – 273
DOI: 10.1049/joe.2017.0872 , Online ISSN 2051-3305
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.
Inspec keywords: MIMO radar; learning (artificial intelligence); inverse problems; direction-of-arrival estimation; Bayes methods
Other keywords: computational load; matched array data; direction-of-arrival estimation estimation; DOA estimation; root SBL algorithm; multiple-output radar system; reduced-dimension transformation; sparse inverse problem; (SBL) framework; monostatic MIMO radar; enhanced sparse Bayesian learning; hyperparameters learning; novel sparse Bayesian; off-grid problem
Subjects: Signal processing and detection; Other topics in statistics; Radar equipment, systems and applications; Other topics in statistics
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