A Super-Resolution Direction of Arrival Estimation Algorithm for Coprime Array via Sparse Bayesian Learning Inference

2017 ◽  
Vol 37 (5) ◽  
pp. 1907-1934 ◽  
Author(s):  
Jie Yang ◽  
Yixin Yang ◽  
Guisheng Liao ◽  
Bo Lei
Keyword(s):  
Bayesian Learning ◽  
Super Resolution ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Direction Of Arrival Estimation ◽  
Sparse Bayesian Learning ◽  
Coprime Array

scholarly journals Underwater Target Direction of Arrival Estimation by Small Acoustic Sensor Array Based on Sparse Bayesian Learning

2017 ◽  
Vol 24 (s2) ◽  
pp. 95-102
Author(s):  
Wang Biao ◽  
He Cheng
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Sparse Bayesian Learning ◽  
Element Space ◽  
Underwater Acoustic

Abstract Assuming independently but identically distributed sources, the traditional DOA (direction of arrival) estimation method of underwater acoustic target normally has poor estimation performance and provides inaccurate estimation results. To solve this problem, a new high-accuracy DOA algorithm based on sparse Bayesian learning algorithm is proposed in terms of temporally correlated source vectors. In novel method, we regarded underwater acoustic source as a first-order auto-regressive process. And then we used the new algorithm of multi-vector SBL to reconstruct the signal spatial spectrum. Then we used the CS-MMV model to estimate the DOA. The experiment results have shown the novel algorithm has a higher spatial resolution and estimation accuracy than other DOA algorithms in the cases of less array element space and less snapshots.

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