A novel adaptive moving average method for signal denoising in strong noise background
Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, 221116, Xuzhou, Jiangsu, China
2 Nonlinear Dynamics, Chaos and Complex Systems Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, 28933, Móstoles, Madrid, Spain
3 School of Mechanical Engineering, Zhejiang University, 310027, Hangzhou, China
Accepted: 7 December 2021
Published online: 22 December 2021
The moving average (MA) method has been widely used in signal processing, but it has problems of the dead zone and the fixed window. In this paper, an adaptive moving average (AMA) filtering method is proposed, which can sniff the inherent characteristics of the signal and assign time-varying optimal parameters to signal processing, hence solve dead zone and the fixed window problems. Firstly, this paper builds the theoretical framework of AMA, including the trial steps and optimization of the necessary parameters. To verify the effectiveness of the AMA, three signal processing methods are taken as comparison methods to process the noisy simulation and experimental signals. Comparison methods includes the MA, variational mode decomposition (VMD), and wavelet threshold denoising (WTD). Signals processed include linear frequency modulation (LFM) simulation signal, aperiodic square wave (ASW) simulation signal, LFM experimental signal produced by a signal generator, and nondestructive test signal of wire rope. Also, the output is analyzed qualitatively and quantitatively with signal-to-noise ratio (SNR), cross-correlation coefficient, amplitude error, and a newly defined local coincidence index. Compared to MA, VMD and WTD, the proposed AMA can solve the dead zone problem, recover noisy signal with higher SNR, cross-correlation coefficient, and lower amplitude error. These results indicate that AMA is a promising method in signal processing.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021