RxCV-based unitary SBL algorithm for off-grid DOA estimation with MIMO radar in unknown non-uniform noise

• Published in: Digital signal processing Vol. 116; p. 103119
• Main Authors: Wang, Huafei, Wang, Xianpeng, Huang, Mengxing, Wan, Liangtian, Su, Ting
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2021
• Subjects: Direction of arrival estimation; Monostatic MIMO radar; Non-uniform noise; Off-grid error; Unitary sparse Bayesian learning; Unitary sparse Bayesian learning; Non-uniform noise; Monostatic MIMO radar; Off-grid error; Direction of arrival estimation
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2021.103119

As an indispensable part of array signal processing, direction-of-arrival (DOA) estimation has been well investigated over the past few decades, and many excellent DOA estimation methods have been proposed. In this paper, a receiving domain covariance vector (RxCV) based unitary SBL algorithm is proposed for the off-grid DOA estimation of monostatic multiple-input multiple-output (MIMO) radar in unknown non-uniform noise environment. In the proposed algorithm, the data received by MIMO radar is firstly transformed into receiving domain by a reshape operation. Then the RxCV-based unitary off-grid sparse model without non-uniform noise is constructed through unitary transformation and first-order linear approximation, where the unknown non-uniform noise is got rid off by a linear transformation. Based on the RxCV-based unitary off-grid sparse model, the sparse Bayesian learning (SBL) criterion is adopted to estimate the parameters, where signal variance and off-gird error are estimated by using expectation-maximization (EM) strategy. The DOA estimation is ultimately realized through 1-dimensional spectrum search of the received data. Results of the simulation experiments have provided the evidence of that the proposed algorithm is robustness against nonuniform noise and off-grid error, and it can maintain superior DOA estimation performance compared with other reported sparse signal representation based methods.