An Improved Sparsity Adaptive Matching Pursuit Algorithm and Its Application in Shock Wave Testing

In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an impro...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-10
Main Authors: Zhang, Jiahui, Wang, Xiao, Ju, Mingchi, Han, Tailin, Wang, Yingzhi
Format: Article
Language:English
Subjects:
Accuracy
Adaptive algorithms
Convex analysis
Efficiency
Greedy algorithms
Matched pursuit
Matching
Mathematical problems
Optimization algorithms
Reconstruction
Redundancy
Shock waves
Sparsity
Stability
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Summary:In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/6615584