scholarly journals Real-valued off-grid DOA estimation based on fourth-order cumulants using sparse Bayesian learning in spatial coloured noise

2019 ◽  
Vol 13 (19) ◽  
pp. 3344-3349
Author(s):  
Meihong Pan ◽  
Gong Zhang ◽  
Zhentao Hu ◽  
Qin Zheng
Keyword(s):  
Fourth Order ◽  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Coloured Noise

Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

2016 ◽  
Vol 129 ◽  
pp. 183-189 ◽  
Author(s):  
Yi Wang ◽  
Minglei Yang ◽  
Baixiao Chen ◽  
Zhe Xiang
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning

scholarly journals Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 302 ◽  
Author(s):  
Yun Ling ◽  
Huotao Gao ◽  
Sang Zhou ◽  
Lijuan Yang ◽  
Fangyu Ren
Keyword(s):  
Autonomous Vehicles ◽  
Bayesian Learning ◽  
Rapid Development ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Vehicle Localization ◽  
Localization System ◽  
Sparse Signal Representation

With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

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

Sparse Bayesian learning for off-grid DOA estimation with nested arrays

2018 ◽  
Vol 82 ◽  
pp. 187-193 ◽  
Author(s):  
Fangfang Chen ◽  
Jisheng Dai ◽  
Nan Hu ◽  
Zhongfu Ye
Keyword(s):  
Bayesian Learning ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Nested Arrays
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