Wireless surface acoustic wave resonator sensors: fast Fourier transform, empirical mode decomposition or wavelets for the frequency estimation in one shot?

Abstract

Most applications which measure physical quantities, especially in harsh environments, rely on surface acoustic wave resonators (SAWRs). Measuring the variation of the resonance frequency is a fundamental step in such cases. This article presents a comparison between three techniques for best determining the resonance frequency in one shot from the point of accuracy and uncertainty: fast Fourier transform (FFT), discrete wavelet transform (DWT) and empirical mode decomposition (EMD). After proposing a model for the generation of synthetic SAW signals, the question of wavelet choice is answered. The three techniques are applied to synthetic signals with different central frequencies and signal-to-noise ratios (SNRs). They are also tested on experimental signals with different sampling rates, number of samples and SNRs. Results are discussed in terms of the accuracy of the estimated frequency and measurement uncertainty. This study is successfully extended to SAWR temperature sensors.