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

Abstract

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.

Active Vibration Control of Robot Gear System Based on Adaptive Control Algorithm

“…In addition, he used an adaptive feedback controller to output the force that determines the amplitude and phase to drive the actuator on the axis in order to reduce the vibration of the box. And the results showed that the vibration decay at the base frequency was about 20-28 dB [5]. Wang, R. et al proposed a new control scheme, in which three magnetostrictive actuators are applied directly on the gear body and the actuator generates a circumferential force to suppress the torsional vibration according to the corresponding control strategy.…”

An Adaptive Sparsity Estimation KSVD Dictionary Construction Method for Compressed Sensing of Transient Signal

“…Since the CS reconstruction phase has the same mathematical model as KSVD in the sparse coding phase, many reconstruction algorithms based on adaptive sparse strategies [22,24] are also derived, which can all be applied to the improvement of KSVD dictionary training methods. We summarize the advantages and disadvantages of related works and propose an ASE-KSVD algorithm that combines the estimation method of sparsity in greedy iteration [25][26][27]. e aim is to improve the dictionary training efficiency and reduce the sparsity error while maintaining a lower sparsity.…”