Perturbation Analysis of ObSTP for Compressed Sensing

Citation Author(s):
Yongfei
Liu
Qinghai Normal University
Submitted by:
Fei Liu
Last updated:
Sat, 12/10/2022 - 18:03
DOI:
10.21227/ab9k-bg47
Data Format:
*.avi; *.csv; *.txt; *.zip
License:
Creative Commons Attribution
43 Views
Categories:
Signal Processing
Keywords:
Compressed Sensingļ¼ŒObSTP algorithms
0
0 ratings - Please login to submit your rating.
ACCESS DATASET CITE SHARE/EMBED

Abstract 

Many algorithms for compressed sensing are studied. The common guarantee for the reconstruction algorithm is restricted isometry property (RIP), which is shown to only hold under ideal assumptions. However, in practice, more than one ideal condition is often violated and there is no RIP-based guarantee application. Based on this discrepancy, we propose a new oblique subspace thresholding pursuit (ObSTP) algorithm. It is guaranteed by the restricted biorthogonality property (RBOP) which requires no ideal assumptions. The ObSTP is an integration of the oblique pursuits and the subspace thresholding pursuit technique. The simulation results show that the ObSTP algorithm has better performance.

Instructions: 

Many algorithms for compressed sensing are studied. The common guarantee for the reconstruction algorithm is restricted isometry property (RIP), which is shown to only hold under ideal assumptions. However, in practice, more than one ideal condition is often violated and there is no RIP-based guarantee application. Based on this discrepancy, we propose a new oblique subspace thresholding pursuit (ObSTP) algorithm. It is guaranteed by the restricted biorthogonality property (RBOP) which requires no ideal assumptions. The ObSTP is an integration of the oblique pursuits and the subspace thresholding pursuit technique. The simulation results show that the ObSTP algorithm has better performance.

Dataset Files

    Files have not been uploaded for this dataset

     

    QUESTIONS?