scholarly journals Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar With Unknown Mutual Coupling

2019 ◽  
Vol 67 (1) ◽  
pp. 208-220 ◽  
Author(s):  
Peng Chen ◽  
Zhenxin Cao ◽  
Zhimin Chen ◽  
Xianbin Wang
Keyword(s):  
Mutual Coupling ◽  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning

scholarly journals Off-Grid DOA Estimation for Colocated MIMO Radar via Reduced-Complexity Sparse Bayesian Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 99907-99916 ◽  
Author(s):  
Tingting Liu ◽  
Fangqing Wen ◽  
Lei Zhang ◽  
Ke Wang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Reduced Complexity

scholarly journals DOA estimation for monostatic MIMO radar using enhanced sparse Bayesian learning

2018 ◽  
Vol 2018 (5) ◽  
pp. 268-273 ◽  
Author(s):  
Fangqing Wen ◽  
Dongmei Huang ◽  
Ke Wang ◽  
Lei Zhang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Sparse Bayesian Learning

scholarly journals Sparse Bayesian Learning for DOA Estimation with Mutual Coupling

Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 26267-26280 ◽  
Author(s):  
Jisheng Dai ◽  
Nan Hu ◽  
Weichao Xu ◽  
Chunqi Chang
Keyword(s):  
Mutual Coupling ◽  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning

scholarly journals A Sparse Bayesian Learning-Based DOA Estimation Method With the Kalman Filter in MIMO Radar

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 347
Author(s):  
Song Liu ◽  
Lan Tang ◽  
Yechao Bai ◽  
Xinggan Zhang
Keyword(s):  
Kalman Filter ◽  
Fast Moving ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Estimation Problem ◽  
Sparse Bayesian Learning ◽  
Moving Targets

The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

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