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.
基金
This work was supported by the National Natural Science Foundation of China under Grants No.61871083 and No.61721001.
作者简介
Cai-Yi Tang,e-mail:caiyi_tang1995@163.com;Sheng Peng,e-mail:pengsheng.com;Corresponding author:Zhi-Qin Zhao,e-mail:zqzhao@uestc.edu.cn;Bo Jiang,e-mail:yavazhili@gmail.com。
