Analysis of the Orthogonal Matching Pursuit Algorithm with Prior Information

2017 ◽  
Vol 96 (1) ◽  
pp. 1495-1506
Author(s):  
Zhilin Li ◽  
Wenbo Xu ◽  
Yupeng Cui ◽  
Jiaru Lin
Keyword(s):  
Prior Information ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Matching Pursuit Algorithm

Least Support Orthogonal Matching Pursuit Algorithm With Prior Information

2014 ◽  
Vol 6 (2) ◽  
pp. 111-134 ◽  
Author(s):  
Israa Sh. Tawfic ◽  
Sema Koc Kayhan
Keyword(s):  
Numerical Experiments ◽  
Prior Information ◽  
Matching Pursuit ◽  
Recovery Process ◽  
Orthogonal Matching Pursuit ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Signal Recovery ◽  
Reconstruction Performance ◽  
Matching Pursuit Algorithm

Abstract This paper proposes a new fast matching pursuit technique named Partially Known Least Support Orthogonal Matching Pursuit (PKLS-OMP) which utilizes partially known support as a prior knowledge to reconstruct sparse signals from a limited number of its linear projections. The PKLS-OMP algorithm chooses optimum least part of the support at each iteration without need to test each candidate independently and incorporates prior signal information in the recovery process. We also derive sufficient condition for stable sparse signal recovery with the partially known support. Result shows that inclusion of prior information weakens the condition on the sensing matrices and needs fewer samples for successful reconstruction. Numerical experiments demonstrate that PKLS-OMP performs well compared to existing algorithms both in terms of reconstruction performance and execution time.

Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm

2018 ◽  
Vol 67 (9) ◽  
pp. 2058-2068 ◽  
Author(s):  
Carlos Morales-Perez ◽  
Jose Rangel-Magdaleno ◽  
Hayde Peregrina-Barreto ◽  
Juan Pablo Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez
Keyword(s):  
Induction Motors ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Matching Pursuit Algorithm

scholarly journals A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 231 ◽  
Author(s):  
Hanfei Zhang ◽  
Shungen Xiao ◽  
Ping Zhou
Keyword(s):  
Energy Distribution ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Selection Criterion ◽  
Critical Factor ◽  
Maximum Energy ◽  
Orthogonal Matching Pursuit ◽  
Support Set ◽  
Reconstruction Efficiency ◽  
Matching Pursuit Algorithm

The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

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