Spectral precision of frequency demodulation method: Influence of additive noise on instantaneous angular speed spectral estimation. (1st February 2022)

• Record Type: Journal Article
• Authors: Lizoul, K.; André, H.; Guillet, F.
• Is Part Of: Mechanical systems and signal processing. Volume 164(2022)
• Publication Date: 2022-02-01
• Subjects: Instantaneous angular speed -- Frequency demodulation -- Hilbert transform -- Quadrature decomposition -- Spectral analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108178
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Frequency demodulation method for IAS estimation is presented and analysed. A theoretical model for the influence of noise on IAS spectrum is developed. A statistical approach for threshold setting on IAS spectral components is proposed. The effect of ADC-board quantization noise and encoder resolution is studied. The proposed approach is validated through numerical simulations and experimental data. Abstract: This paper treats the spectral precision of frequency demodulation method based on Gabor's analytic signal applied to Instantaneous Angular Speed (IAS) estimation from speed sensor under stationary speed conditions. The quadrature decomposition combined with geometric complex representation are used to approach the spectral modeling of the influence of wide-sense stationary additive noise on IAS spectral estimate using the periodogram. A method is proposed for IAS noise spectral estimation from signal's envelope. Moreover, the effect of the carrier power and encoder resolution is demonstrated through real life signals. Finally, a statistical approach is proposed in order to set thresholds on IAS spectral observation allowing a good separation of additive random perturbations from real information under a certain probability. The proposed tools are applied on both synthetic and experimental signals and are shown to be effective in estimating IAS noise level from the input signal properties.