A super resolution DOA estimation method based on sparse signal representation

2014 ◽  
Author(s):  
Ping Cheng ◽  
Tingting Liu ◽  
Shu Wang ◽  
Wei Zhang
Keyword(s):  
Estimation Method ◽  
Super Resolution ◽  
Doa Estimation ◽  
Sparse Signal ◽  
Signal Representation ◽  
Sparse Signal Representation

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.

Quantitative Optoacoustic Signal Extraction Using Sparse Signal Representation

2009 ◽  
Vol 28 (12) ◽  
pp. 1997-2006 ◽  
Author(s):  
A. Rosenthal ◽  
D. Razansky ◽  
V. Ntziachristos
Keyword(s):  
Signal Extraction ◽  
Sparse Signal ◽  
Signal Representation ◽  
Sparse Signal Representation
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