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.

scholarly journals Stochastic Gradient Matching Pursuit Algorithm Based on Sparse Estimation

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 165 ◽  
Author(s):  
Liquan Zhao ◽  
Yunfeng Hu ◽  
Yulong Liu
Keyword(s):  
Prior Information ◽  
Matching Pursuit ◽  
Stochastic Gradient ◽  
Least Square ◽  
Improved Method ◽  
Step Size ◽  
Sparse Estimation ◽  
Practical Applications ◽  
Matching Pursuit Algorithm ◽  
Better Than

The stochastic gradient matching pursuit algorithm requires the sparsity of the signal as prior information. However, this prior information is unknown in practical applications, which restricts the practical applications of the algorithm to some extent. An improved method was proposed to overcome this problem. First, a pre-evaluation strategy was used to evaluate the sparsity of the signal and the estimated sparsity was used as the initial sparsity. Second, if the number of columns of the candidate atomic matrix was smaller than that of the rows, the least square solution of the signal was calculated, otherwise, the least square solution of the signal was set as zero. Finally, if the current residual was greater than the previous residual, the estimated sparsity was adjusted by the fixed step-size and stage index, otherwise we did not need to adjust the estimated sparsity. The simulation results showed that the proposed method was better than other methods in terms of the aspect of reconstruction percentage in the larger sparsity environment.

An Adaptive Invasive Weed Optimization Algorithm

2015 ◽  
Vol 29 (02) ◽  
pp. 1559004 ◽  
Author(s):  
Shuo Peng ◽  
A.-J. Ouyang ◽  
Jeff Jun Zhang
Keyword(s):  
Optimization Algorithm ◽  
Initial Step ◽  
Local Extremum ◽  
Invasive Weed Optimization ◽  
Step Size ◽  
Invasive Weed ◽  
Adaptive Step Size ◽  
Initial Step Size ◽  
Adaptive Step ◽  
Optimization Search

With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.

scholarly journals A Sparsity Preestimated Adaptive Matching Pursuit Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xinhe Zhang ◽  
Yufeng Liu ◽  
Xin Wang
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Initial Value ◽  
Step Size ◽  
Matching Pursuit Algorithm ◽  
Run Speed ◽  
Number Of Iterations

In the matching pursuit algorithm of compressed sensing, the traditional reconstruction algorithm needs to know the signal sparsity. The sparsity adaptive matching pursuit (SAMP) algorithm can adaptively approach the signal sparsity when the sparsity is unknown. However, the SAMP algorithm starts from zero and iterates several times with a fixed step size to approximate the true sparsity, which increases the runtime. To increase the run speed, a sparsity preestimated adaptive matching pursuit (SPAMP) algorithm is proposed in this paper. Firstly, the sparsity preestimated strategy is used to estimate the sparsity, and then the signal is reconstructed by the SAMP algorithm with the preestimated sparsity as the iterative initial value. The method reconstructs the signal from the preestimated sparsity, which reduces the number of iterations and greatly speeds up the run efficiency.

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.

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