Statistical Frequency Estimation Techniques for Vibrating Wire Sensor Signals

Vibrating wire-based sensors are widely used for stress measurement of strata in underground coal mines, dams and bridges. Stress measurement in this sensor is purely based on frequency as it has several advantages compared to other conventional sensors. Thus estimation of frequency plays a crucial role in measuring the stress. This study focuses on comparison of two efficient frequency estimation techniques seeking their application in vibrating wire sensors which are popularly used in coal mines. MUltiple Signal Classification (MUSIC) and Maximum Likelihood Estimation (MLE) methods are simulated for the above mentioned application and estimation accuracy is compared with Cramer Rao Lower Bound(CRLB). Numerical results are presented here to illustrate the attainment of CRLB by above two estimators. Computational complexity expressions are derived as $O(NlogN)$ and $O(N^{3})$ respectively for MLE and MUSIC, where $N$ is the considered window length for signal processing. Overall study helps to conclude that both MLE and MUSIC can be applied to vibrating wire sensor signal for estimating frequency, amplitude and phase.