The Journal of Engineering | |
DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning | |
Fangqing Wen1  Ke Wang2  Dongmei Huang3  | |
[1] Electronic and Information School, Yangtze University , Jingzhou 434023 , People'Information Department , Naval Command College , Nanjing 210016 , People's Republic of China | |
关键词: 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; | |
DOI : 10.1049/joe.2017.0872 | |
学科分类:工程和技术(综合) | |
来源: IET | |
【 摘 要 】
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.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
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RO201910259999466ZK.pdf | 313KB | download |