A new OMP technique for sparse recovery
2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings
Item Usage Stats
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28196
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.
Orthogonal matching pursuit