A new OMP technique for sparse recovery [Seyrek geriçatma i̇çin yeni bir OMP yöntemi]
2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings
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.
- Conference Paper