Title :
High-Speed Signal Reconstruction with Orthogonal Matching Pursuit via Matrix Inversion Bypass
Author :
Guoxian Huang ; Lei Wang
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA
Abstract :
Compressive sensing (CS) is an emerging research area that has great significance to the design of resource-constrained cyber physical systems. Signal reconstruction in CS remains a challenge due to its high computational complexity, which limits the practical application of CS. In this paper, we propose an algorithmic transformation referred to as Matrix Inversion Bypass (MIB) to reduce the computational complexity of the Orthogonal Matching Pursuit(OMP) based CS reconstruction. The proposed algorithm naturally leads to a parallel architecture for high-speed dedicated hardware implementations. Furthermore, by applying the proposed MIB, the energy consumption of CS reconstruction can be reduced as well. This is vital to many cyber-physical systems that are powered by batteries or renewable energy sources. Simulation results demonstrate the advantages of the proposed technique over the conventional OMP algorithm.
Keywords :
compressed sensing; computational complexity; iterative methods; matrix inversion; parallel architectures; signal reconstruction; MIB; OMP algorithm; OMP based CS reconstruction; algorithmic transformation; batteries; compressive sensing; computational complexity; energy consumption; high-speed signal reconstruction; matrix inversion bypass; orthogonal matching pursuit; parallel architecture; renewable energy source; resource-constrained cyber physical system design; Complexity theory; Hardware; Image reconstruction; Indexes; Matching pursuit algorithms; Transforms; Vectors; compressive sensing; cyber-physical systems; orthogonal matching pursuit; signal reconstruction;
Conference_Titel :
Signal Processing Systems (SiPS), 2012 IEEE Workshop on
Conference_Location :
Quebec City, QC
Print_ISBN :
978-1-4673-2986-6
DOI :
10.1109/SiPS.2012.26