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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiahui Zhang ◽  
Xiao Wang ◽  
Mingchi Ju ◽  
Tailin Han ◽  
Yingzhi Wang
Keyword(s):  
Shock Wave ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Estimation Accuracy ◽  
Variable Step Size ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Step Size ◽  
Candidate Set ◽  
Double Threshold

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.

scholarly journals Filtering-Based Regularized Sparsity Variable Step-Size Matching Pursuit and Its Applications in Vehicle Health Monitoring

2021 ◽  
Vol 11 (11) ◽  
pp. 4816
Author(s):  
Haoqiang Liu ◽  
Hongbo Zhao ◽  
Wenquan Feng
Keyword(s):  
Health Monitoring ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Superior Performance ◽  
Sparse Signal ◽  
Variable Step Size ◽  
Step Size ◽  
Signal Sampling ◽  
Reconstruction Performance ◽  
Sparsity Level

Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods.

scholarly journals Image reconstruction algorithm based on variable atomic number matching pursuit

2016 ◽  
Vol 11 (2) ◽  
pp. 103-109
Author(s):  
Hongtu Zhao ◽  
Chong Chen ◽  
Chenxu Shi
Keyword(s):  
Image Reconstruction ◽  
Convex Optimization ◽  
Atomic Number ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Optimization Algorithms ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Reconstruction Quality ◽  
Stable Reconstruction

As the most critical part of compressive sensing theory, reconstruction algorithm has an impact on the quality and speed of image reconstruction. After studying some existing convex optimization algorithms and greedy algorithms, we find that convex optimization algorithms should possess higher complexity to achieve higher reconstruction quality. Also, fixed atomic numbers used in most greedy algorithms increase the complexity of reconstruction. In this context, we propose a novel algorithm, called variable atomic number matching pursuit, which can improve the accuracy and speed of reconstruction. Simulation results show that variable atomic number matching pursuit is a fast and stable reconstruction algorithm and better than the other reconstruction algorithms under the same conditions.

scholarly journals Improved Generalized Sparsity Adaptive Matching Pursuit Algorithm Based on Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhao Liquan ◽  
Ma Ke ◽  
Jia Yanfei
Keyword(s):  
Matching Pursuit ◽  
Initial Step ◽  
Convergence Speed ◽  
One Dimension ◽  
Measurement Matrix ◽  
Improved Method ◽  
Reconstruction Accuracy ◽  
Step Size ◽  
Matching Pursuit Algorithm ◽  
Initial Step Size

The modified adaptive orthogonal matching pursuit algorithm has a lower convergence speed. To overcome this problem, an improved method with faster convergence speed is proposed. In respect of atomic selection, the proposed method computes the correlation between the measurement matrix and residual and then selects the atoms most related to residual to construct the candidate atomic set. The number of selected atoms is the integral multiple of initial step size. In respect of sparsity estimation, the proposed method introduces the exponential function to sparsity estimation. It uses a larger step size to estimate sparsity at the beginning of iteration to accelerate the algorithm convergence speed and a smaller step size to improve the reconstruction accuracy. Simulations show that the proposed method has better performance in terms of convergence speed and reconstruction accuracy for one-dimension signal and two-dimension signal.

Signal Reconstruction Based on a Fusion Compressed Sensing Frame

2013 ◽  
Vol 785-786 ◽  
pp. 1315-1323
Author(s):  
Xu Hua Li ◽  
Yue Li Chen ◽  
Nan Jun Hu ◽  
Wei Li ◽  
Tian Jun Yuan ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Greedy Algorithms ◽  
Reconstruction Error ◽  
Reconstruction Accuracy ◽  
Support Sets ◽  
Reconstruction Effects ◽  
Better Than

Greedy algorithms represented by orthogonal matching pursuit (OMP) and subspace pursuit (SP) algorithms are practically used in image processing based upon compressed sensing theory. However, there are two disadvantages: 1)Relatively poor signal reconstruction accuracy; 2) High computation complexity and measurements time. This paper proposes a frame of greedy algorithms obtaining a novel fusion of matching pursuit (FMP), combining the OMP and SP algorithms. FMP unites the two support sets from OMP and SP selecting the most appropriate atoms to achieve secondary screening of the original two support sets, finally realizing the accurate signal reconstruction. Using same test conditions, image reconstruction experiments and stability of Frame, the proposed FMP algorithm can effectively improve signal-to-noise ratio (SNR) with improved reconstruction error. Reconstruction effects using proposed FMP are better than separately using other two greedy algorithms for both high and low resolution images.

A Numerical Study on Shock Wave Characteristics for the Underwater Explosion

2012 ◽  
Vol 594-597 ◽  
pp. 2680-2683
Author(s):  
Wei Ju ◽  
Yi Liu ◽  
Jue Ding
Keyword(s):  
Shock Wave ◽  
Numerical Study ◽  
Bubble Formation ◽  
Underwater Explosion ◽  
Scientific Basis ◽  
Energy Output ◽  
Damage Effect ◽  
Variable Step Size ◽  
Step Size ◽  
Output Characteristics

Underwater explosion is very important for underwater weapons-design technology and research on the damage effect of target structure. In this paper, the flow-out boundary and variable step-size multi-material Euler algorithm were utilized to analyze numerically the whole process of shock wave generation and propagation, as well as bubble formation and impulse of underwater explosion. The computed results reveal the energy output characteristics of underwater explosion by TNT charge, which provide an important scientific basis for formulation design of charge and improvement of damage effects for underwater target.

Variable step-size matching pursuit based on oblique projection for compressed sensing

2020 ◽  
Vol 14 (4) ◽  
pp. 766-773
Author(s):  
Na Li ◽  
Xinghui Yin ◽  
Huanyin Guo ◽  
Sulan Zong ◽  
Wei Fu
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Variable Step Size ◽  
Oblique Projection ◽  
Step Size ◽  
Variable Step

scholarly journals An Adaptive Fuzzy Neural Network Based on Progressive Gaussian Approximate Filter with Variable Step Size

2021 ◽  
Author(s):  
Guorui Zhu
Keyword(s):  
Neural Network ◽  
Nonlinear Filtering ◽  
Hot Spot ◽  
Estimation Accuracy ◽  
Variable Step Size ◽  
Step Size ◽  
Adaptive Fuzzy ◽  
Neural Network Controller ◽  
Variable Step ◽  
Filtering Problem

Abstract The nonlinear filtering problem is a hot spot in robot navigation research. Based on this idea, I focus on how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. In order to improve the estimation accuracy of the progressive Gaussian approximate filter with variable step size(PGAFVS), this paper selects the optimal values in practical applications and proposed an adaptive fuzzy and neural network controller. The controller, as well as the measurement noise covariance matrix, is jointly estimated based on the PGAF, from which the PGAFVS is developed. The simulation results show that the proposed algorithm outperforms the state of the art methods.

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