Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

作     者：Wang Chuanchuan Zeng Yonghu Wang Liandong Fu Weihong 

作者机构：State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information SystemLuoyang 471003China State Key Laboratory of Integrated Services NetworksXidian UniversityXi'an 710071China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2019年第26卷第6期

页      面：94-102页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

主　　题：underdetermined blind source separation signal recovery ISSDP 

摘      要：Aiming at the statistical sparse decomposition principle(SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP(ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix’s column vectors used for signal recovery is modified, which enables the choose of mixing matrix’s column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.