A new OMP technique for sparse recovery
Author
Teke, Oğuzhan
Gürbüz, A.C.
Arıkan, Orhan
Date
2012Source Title
2012 20th Signal Processing and Communications Applications Conference (SIU)
Publisher
IEEE
Language
Turkish
Type
Conference PaperItem Usage Stats
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Show full item recordAbstract
Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. However in reality there is a mismatch between the assumed and the actual bases due to several reasons like discritization of the parameter space or model errors. Due to this mismatch, a sparse signal in the actual basis is definitely not sparse in the assumed basis and current sparse reconstruction algorithms suffer performance degradation. This paper presents a novel orthogonal matching pursuit algorithm that has a controlled perturbation mechanism on the basis vectors, decreasing the residual norm at each iteration. Superior performance of the proposed technique is shown in detailed simulations. © 2012 IEEE.
Keywords
Basis vectorCompressive sensing
Controlled perturbation
Model errors
Orthogonal matching pursuit
Parameter spaces
Performance degradation
Residual norm
Sparse reconstruction
Sparse recovery
Sparse signals
Channel estimation
Degradation
Signal processing