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

作　　者：Cai-Yi Tang Sheng Peng Zhi-Qin Zhao Bo Jiang 

机构地区：[1]Science and Technology on Electronic Information Control Laboratory,Chengdu 610036 [2]School of Electronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731 [3]54th Research Institute of China Electronics Technology Group,Shijiazhuang 050081

出　　处：《Journal of Electronic Science and Technology》2022年第4期416-424,共9页电子科技学刊（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China under Grants No.61871083 and No.61721001.

摘　　要：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.

关 键 词：Compressive sensing(CS) randomize-then-optimize(RTO) single snapshot sparse signal reconstruction 

分 类 号：TN911.7[电子电信—通信与信息系统]