Robust Reconstruetion of Block Sparse Signals from Adaptively One-Bit Measurements  

作　　者：HOU Jingyao WANG Jianjun ZHANG Feng HUANG Jianwen 

机构地区：[1]School of Mathematics and Statistics,Southwest University,Chongqing 400715,China [2]College of Artificial Intelligence,Southwest University,Chongqing 400715,China [3]School of Mathematics and Statistics,Ningxia University,Yinchuan 750021,China [4]School of Mathematics and Statistics,Tianshui Normal University,Tianshui 741000,China

出　　处：《Chinese Journal of Electronics》2020年第5期937-944,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61673015,No.61273020);the Fundamental Research Funds for the Central Universities(No.XDJK2018C076,No.SWU1809002).

摘　　要：Though various theoretical results and algorithms have been proposed in one-bit Compressed sensing(l・bit CS),t here are few stu dies on more structured signals,such as block sparse signals.We address the problem of recovering block sparse signals from one-bit measurements.We first propose two recovery schemes,one based on second-order cone programming and the other based on hard thresholding,for common non-adaptively thresholded one-bit measurements.Note that the worst・case error in recovering sparse signals from non・adaptively thresholded one-bit measurements is bounded below by a polynomial of oversampling factor.To break the limit,we introduce a recursive strategy that allows the thresholds in quantization to be adaptive to previous measurements at each it eration.Using the scheme,we propose two iterative algorithms and show that corresponding recovery errors are both exponential functions of the oversampling factor.Several simulations are conducted to reveal the superiority of our methods to existing approaches.

关 键 词：One-bit compressed sensing Convex programming Hard thresholding Adaptivity Iterative algorithms. 

分 类 号：TN911.7[电子电信—通信与信息系统]