The direction-of-arrival (DOA) estimation problem can be solved by the methods based on sparse Bayesian learning (SBL). To assure the accuracy, SBL needs massive amounts of snapshots which may lead to a huge computational workload. In order to reduce the snapshot number and computational complexity, a randomize-then-optimize (RTO) algorithm based DOA estimation method is proposed. The"learning"process for updating hyperparameters in SBL can be avoided by using the optimization and Metropolis-Hastings process in the RTO algorithm. To apply the RTO algorithm for a Laplace prior, a prior transformation technique is induced. To demonstrate the effectiveness of the proposed method, several simulations are proceeded, which verifies that the proposed method has better accuracy with 1 snapshot and shorter processing time than conventional compressive sensing (CS) based DOA methods.

Cai-Yi Tang;Sheng Peng;Zhi-Qin Zhao;Bo Jiang

Science and Technology on Electronic Information Control Laboratory,Chengdu 610036;School of Electronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731;4th Research Institute of China Electronics Technology Group,Shijiazhuang 050081

电子科技学刊

[1]Cai-Yi Tang;Sheng Peng;Zhi-Qin Zhao;Bo Jiang-.Direction-of-Arrival Method Based on Randomize-Then-Optimize Approach)[J].电子科技学刊,2022(04):416-424

Randomize,randomize

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