High-Accurate Robust Total Variation Denoising Algorithm with Adjustable Exponential Upper Bound Function for Micro-Thrust Measurement

Micro-Newton thrusters are widely utilized in the field of astronautics. Typically, micro-Newton thrust signal processing aims at the restoration of piecewise constant signals (PCSs). Improving the amplitude accuracy of the denoised signal remains a major difficulty. In this research, we proposed a new nonlinear total variation denoising (TVD) algorithm with an adjustable majorization-minimization (MM) upper bound function for optimizing the iterative solution process of TVD. The influences of the upper bound function of the TVD on denoising are analyzed in detail. It is demonstrated that the proposed adjustable parameter-a -based TVD (aTVD) method shows extraordinary robustness for denoising signals with different noise levels. In addition, this algorithm can also adjust the sensitivity of the jumps in signals for optimized amplitude accuracy effectively. Numerical examples show that the proposed method has 40.4% and 18.2% accuracy improvement over conventional TVD using traditional quadratic upper bound and enhanced TVD using exponential upper bound function. Moreover, the advantages of the proposed method are validated in an experiment of measured thrust signal processing. The proposed method can be further adopted to improve the performance of signal denoising algorithms developed from the TVD method.