DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning

The Journal of Engineering

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