title: Recoverability analysis for modified compressive sensing with partially known support.
abstract: The recently proposed modified-compressive sensing (modified-CS), which utilizes the partially known support as prior knowledge, significantly improves the performance of recovering sparse signals. However, modified-CS depends heavily on the reliability of the known support. An important problem, which must be studied further, is the recoverability of modified-CS when the known support contains a number of errors. In this letter, we analyze the recoverability of modified-CS in a stochastic framework. A sufficient and necessary condition is established for exact recovery of a sparse signal. Utilizing this condition, the recovery probability that reflects the recoverability of modified-CS can be computed explicitly for a sparse signal with [Formula: see text] nonzero entries. Simulation experiments have been carried out to validate our theoretical results.
authors: College of Information Engineering, Guangdong University of Technology, Guangzhou, People's Republic of China.
authors: Jun
authors: J
authors: Zhang
authors: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, People's Republic of China.
authors: Yuanqing
authors: Y
authors: Li
authors: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, People's Republic of China.
authors: Zhenghui
authors: Z
authors: Gu
authors: Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, People's Republic of China.
authors: Zhu Liang
authors: ZL
authors: Yu
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis; D003198:Computer Simulation; D044962:Data Compression; D006801:Humans; D008962:Models, Theoretical; D011336:Probability; D012815:Signal Processing, Computer-Assisted