Title of article :
A reducing iteration orthogonal matching pursuit algorithm for compressive sensing
Author/Authors :
Wang, Rui School of Computer and Communication Engineering - University of Science and Technology Beijing , Zhang, Jinglei School of Computer and Control Engineering, University of Chinese Academy of Sciences , Ren, Suli School of Computer and Communication Engineering - University of Science and Technology Beijing , Li, Qingjuan School of Computer and Communication Engineering - University of Science and Technology Beijing
Abstract :
In recent years, Compressed Sensing (CS) has been a hot research topic. It has a wide range of
applications, such as image processing and speech signal processing owing to its characteristic of removing
redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations
in a greedy algorithm such as Orthogonal Matching Pursuit (OMP) is fixed, thus limiting reconstruction precision.
Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit (RIOMP) algorithm
that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations.
The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses.
When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the
proposed algorithm can accurately reconstruct signals in a shorter running time.
Keywords :
wireless sensor networks , signal processing , compressed sensing
Journal title :
Astroparticle Physics
