Sparse Signal Recovery via Iterative Detection Estimation with Thresholding
Song He-ping① Wang Guo-li②
①(School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, China) ②(School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China)
Abstract：This paper devotes efforts to develope sparse signal recovery algorithms for Compressed Sensing (CS). The proposed algorithmic framework is called as Iterative Detection Estimation with Thresholding (IDET). IDET takes the One-Stage Thresholding (OST) as the reference for support detection, and devises support detection methods depended on the character of sparse signals. This study presents an implementation of IDET, which detects a support set by thresholding the result of the Iterative Hard Thresholding (IHT) iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support set, and it iterates these two steps until stop condition is met. The key to IDET lies in support detection, so this study explores three support detection strategies for fast decaying signals. The experimental results show IDET exhibits superior reconstruction performance than other accelerated algorithm for IHT.
宋和平, 王国利. 稀疏信号重构的阈值化迭代检测估计[J]. 电子与信息学报, 2014, 36(10): 2431-2437.
Song He-Ping, Wang Guo-Li. Sparse Signal Recovery via Iterative Detection Estimation with Thresholding. , 2014, 36(10): 2431-2437.
2014, Vol. 36 
