Abstract
Resonant MEMS sensors have potential of providing highly precise measurements. The accurate processing of the output relies on precise frequency estimation techniques especially in the context of portable sensors for particulate matter. This paper investigates five commonly used single-tone frequency estimation techniques (3-point DFT interpolation, parabolic interpolation of periodogram peak, Prony's method, modified Pisarenko and zero crossing method) with respect to the estimation accuracy, memory requirement and computational complexity. The effect of noise and harmonics on estimation accuracy of these five techniques are analyzed and validated through simulation. The experimental data is acquired from a resonant MEMS sensor with a center frequency of 3.15 MHz. The output is sampled at 100MS/s using a 12-bit ADC. These five techniques are applied to the various data sets acquired from an experimental setup. The comparison results along with the analysis are presented.
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