Improved adaptive forward-backward matching pursuit algorithm to compressed sensing signal recovery

2019 ◽  
Vol 78 (23) ◽  
pp. 33969-33984
Author(s):  
Zong Meng ◽  
Zuozhou Pan ◽  
Ying Shi ◽  
Zijun Chen
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Signal Recovery ◽  
Matching Pursuit Algorithm

Array DOA Estimation by the Model of Four Elements in Quare Array Based on the Theory of Compressed Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 971-977 ◽  
Author(s):  
Rui Yuan ◽  
Jun Yue ◽  
Hong Xiu Gao
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Doa Estimation ◽  
Low Computational Complexity ◽  
Matching Pursuit Algorithm ◽  
Square Array

The DOA estimation by the model of four elements in the square array has studied based on the theory of compressed sensing. Using matching pursuit algorithm and orthogonality matching pursuit algorithm, the computer simulation was presented. The results show the method of DOA estimation by compressed sensing theory is simple, practical and low computational complexity.

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.

scholarly journals Reconstruction Technique Based on the Theory of Compressed Sensing Satellite Images

2015 ◽  
Vol 9 (1) ◽  
pp. 74-81
Author(s):  
Wang Feng ◽  
Chen Feng-wei ◽  
Wang Jia
Keyword(s):  
Compressed Sensing ◽  
Satellite Images ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Technical Problem ◽  
Reconstruction Technique ◽  
Satellite Image Processing ◽  
Matching Pursuit Algorithm ◽  
Compression Sensing

Owing to the characteristics such as high resolution, large capacity, and great quantity, thus far, how to efficient store and transmit satellite images is still an unsolved technical problem. Satellite image Compressed sensing (CS) theory breaks through the limitations of traditional Nyquist sampling theory, it is based on signal sparsity, randomness of measurement matrix and nonlinear optimization algorithms to complete the sampling compression and restoring reconstruction of signal. This article firstly discusses the study of satellite image compression based on compression sensing theory. It then optimizes the widely used orthogonal matching pursuit algorithm in order to make it fits for satellite image processing. Finally, a simulation experiment for the optimized algorithm is carried out to prove this approach is able to provide high compression ratio and low signal to noise ratio, and it is worthy of further study.

scholarly journals Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257
Author(s):  
Renjie Xu ◽  
Ting Yun ◽  
Lin Cao ◽  
Yunfei Liu
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Morphological Attributes ◽  
Forest Inventories ◽  
Matching Pursuit Algorithm ◽  
Fourier Matrix ◽  
Coordinate Data

The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

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