Improved Compressed Sensing Reconfiguration Algorithm with Shockwave Dynamic Compensation Features

Abstract

This paper proposes a regularized generalized orthogonal matching pursuit algorithm with dynamic compensation characteristics based on the application context of compressive sensing in shock wave signal testing. We add dynamic compensation denoising as a regularization condition to the reconstruction algorithm. The resonant noise is identified and suppressed according to the signal a priori characteristics, and the denoised signal is reconstructed directly from the original signal downsampling measurements. The signal-to-noise ratio of the output signal is improved while reducing the amount of data transmitted by the signal. The proposed algorithm’s applicability and internal parameter robustness are experimentally analyzed in the paper. We compare the proposed algorithm with similar compression-aware reconstruction and dynamic compensation algorithms under the shock tube test and measured shock wave signals. The results from the reconstruction signal-to-noise ratio and the number of measurements required for reconstruction verify the algorithm’s effectiveness in this paper.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

“…Reference [10] proposed minimizing the entropy corresponding to the echo signals as the objective function and using the Particle Swarm Optimization (PSO) algorithm to search for global optimum parameters to achieve compensation of joint motion signals. Reference [11] introduced a dynamic compensation feature in the Regularized Generalized Orthogonal Matching Pursuit algorithm, which identifies and suppresses resonant noise based on the signal's priori features and directly reconstructs denoised signals from subsampled measurements of the original signal. Reference [12] presented a signal compensation method that combines the Sparse Fast Fourier Transform (SFFT) and the Iterative Adaptive Approach (IAA).…”

“…References [10,11] both proposed methods based on parameterized compensation and calibration; however, since radar measurement of accumulator piston displacement usually occurs in a high signal-to-noise ratio environment, this makes compensation challenging. References [12,13] both employed feature extraction methods to extract characteristics, such as frequency, amplitude, and phase, from rolling eccentricity signals, but rolling eccentricity signals and accumulator piston displacement signals differ in source, characteristics, and application scenarios.…”