Open AccessDOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning
Author(s) -
Wen Fangqing,
Huang Dongmei,
Wang Ke,
Zhang Lei
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0872
Subject(s) - computer science , hyperparameter , algorithm , grid , direction of arrival , mimo , radar , dimension (graph theory) , snapshot (computer storage) , compressed sensing , bayesian probability , artificial intelligence , mathematics , telecommunications , beamforming , geometry , antenna (radio) , pure mathematics , operating system
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.