DOA Estimation Based on Root Sparse Bayesian Learning Under Gain and Phase Error  

作　　者：Dingke Yu Xin Wang Wenwei Fang Zixian Ma Bing Lan Chunyi Song Zhiwei Xu 

机构地区：[1]Institute of Marine Electronic and Intelligent System.Ocean College.Zhejiang University.Zhoushan 316021,China [2]Engineering Research Center of Oceanic Sensing Technology and Equipment,the Ministry of Education.Zhoushan 316021,China.Donghai Lab,Zhoushan 316021.China

出　　处：《Journal of Communications and Information Networks》2022年第2期202-213,共12页通信与信息网络学报（英文）

基　　金：National Natural Sci-ence Foundation of China(NSFC)(61971379);Key Research and Development Program of Zhejiang Province(2020C03100);Leading Innovative and Entrepreneur Team In-troduction Program of Zhejiang(2018R01001);Fundamental Research Funds for the Central Universities(226202200096);Program of Innovation 2030 on Smart Ocean in Zhejiang University(129000*194232201)。

摘　　要：The direction of arrival(DOA)is approximated by first-order Taylor expansion in most of the existing methods,which will lead to limited estimation accuracy when using coarse mesh owing to the off-grid error.In this paper,a new root sparse Bayesian learning based DOA estimation method robust to gain-phase error is proposed,which dynamically adjusts the grid angle under coarse grid spacing to compensate the off-grid error and applies the expectation maximization(EM)method to solve the respective iterative formula-based on the prior distribution of each parameter.Simulation results verify that the proposed method reduces the computational complexity through coarse grid sampling while maintaining a reasonable accuracy under gain and phase errors,as compared to the existing methods.

关 键 词：direction of arrival estimation gain-phase error root sparse Bayesian learning off-grid error 

分 类 号：TN92[电子电信—通信与信息系统]