scholarly journals Real‐valued gridless DOA estimation in massive ULA using a single snapshot

2021 ◽  
Vol 57 (8) ◽  
pp. 343-346
Author(s):  
Jiawen Yuan ◽  
Gong Zhang ◽  
Wenbo Liu ◽  
Xinhai Wang ◽  
Changyun Qi
Keyword(s):  
Doa Estimation ◽  
Single Snapshot

Orthogonal Projection DOA Estimation with a Single Snapshot

2013 ◽  
Vol E96.B (5) ◽  
pp. 1215-1217 ◽  
Author(s):  
Ann-Chen CHANG ◽  
Chih-Chang SHEN
Keyword(s):  
Orthogonal Projection ◽  
Doa Estimation ◽  
Single Snapshot

Multi-source Off-grid DOA Estimation with Single Snapshot using Non-uniform Linear Arrays

2021 ◽  
pp. 108238
Author(s):  
Yanan Ma ◽  
Xianbin Cao ◽  
Xiangrong Wang ◽  
Maria S Greco ◽  
Fulvio Gini
Keyword(s):  
Doa Estimation ◽  
Linear Arrays ◽  
Single Snapshot

Single snapshot DOA estimation by compressive sampling

2013 ◽  
Vol 74 (7) ◽  
pp. 926-930 ◽  
Author(s):  
Xuan Li ◽  
Xiaochuan Ma ◽  
Shefeng Yan ◽  
Chaohuan Hou
Keyword(s):  
Doa Estimation ◽  
Compressive Sampling ◽  
Single Snapshot

scholarly journals Capon-based single-snapshot DOA estimation in monostatic MIMO radar

2015 ◽  
Author(s):  
Aboulnasr Hassanien ◽  
Moeness G. Amin ◽  
Yimin D. Zhang ◽  
Fauzia Ahmad
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Single Snapshot

scholarly journals Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach

2020 ◽  
Author(s):  
Caiyi Tang ◽  
Qianli Wang ◽  
Zhiqin Zhao ◽  
Zaiping Nie ◽  
Chuanfeng Niu
Keyword(s):  
Processing Time ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Bayesian Learning ◽  
Transformation Technique ◽  
Computational Burden ◽  
First Time ◽  
Single Snapshot

Abstract Sparse Bayesian learning (SBL) has been successfully applied in solving the problem of direction-of-arrival (DOA) estimation. However, SBL needs multiple snapshots to ensure accuracy and costs huge computational workload. To reduce the requirement of snapshot and computational burden, a DOA estimation method based on the randomize-then-optimize (RTO) algorithm is first time introduced. RTO algorithm uses the optimization and Metropolis-Hastings approach to avoid the “learning” process of SBL in updating hyperparameters. And in order to apply RTO algorithm in the circumstance of signal with Laplace prior, a prior transformation technique is first induced. Compared with conventional CS based DOA methods, the proposed method has a better accuracy with single snapshot and shorter processing time. Some simulations are conducted to demonstrate the effectiveness of the proposed method.

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