Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation  

作　　者：魏爽 李文瑶 苏颖 刘睿 WEI Shuang;LI Wenyao;SU Ying;LIU Rui(College of Information,Mechanical and Electrical Engineering,Shanghai Engineering Research Center of Intelligent Education and Bigdata,Shanghai Normal University,Shanghai 200234,China)

机构地区：[1]College of Information,Mechanical and Electrical Engineering,Shanghai Engineering Research Center of Intelligent Education and Bigdata,Shanghai Normal University,Shanghai 200234,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2023年第6期763-771,共9页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(No.61401145);the Natural Science Foundation of Shanghai(No.19ZR1437600)。

摘　　要：For dense time delay estimation(TDE),when multiple time delays are located within a grid interval,it is dificult for the existing sparse Bayesian learning/inference(SBL/SBI)methods to obtain high estimation accuracy to meet the application requirements.To solve this problem,this paper proposes a method named off-grid sparse Bayesian inference-biased total grid(OGSBI-BTG),where a mesh evolution process is conducted to move the total grids iteratively based on the position of the off-grid between two grids.The proposed method updates the off-grid dictionary matrix by further reconstructing an optimum mesh and offsetting the off-grid vector.Experimental results demonstrate that the proposed approach performs better than other state-of-the-art SBI methods and multiple signal classification even when the grid interval is larger than the gap of true time delays.In this paper,the time domain model and frequency domain model of TDE are studied.

关 键 词：OFF-GRID sparse Bayesian inference(SBI) time delay estimation(TDE) biased total grids(BTG) 

分 类 号：TN928[电子电信—通信与信息系统]