Wavelet spectral techniques for error mitigation of superconductive angular accelerometer output

English Abstract:

A superconductive angular accelerometer is an important sensor that often supplements a superconductive angular gradiometer on a moving platform during data acquisition operations.. The gradiometer instrument senses not only gravity gradients, but also various effects of an accelerated coordinate frame (which are introducing major errors). The superconductive angular accelerometer measurements help compensate the main error, which is entering as angular velocity squared in the gradiometer output. While the angular accelerations can be measured, angular velocity squared has to be computed by integration. However, a major difficulty arises when the angular accelerations are noisy because of translational accelerations of the platform and temporal fluctuations of the environment during the operation. These errors are a source of low and high frequency error in the accelerometer output. Therefore, filtering of angular accelerations is necessary before integrating them to produce angular velocities. We have implemented wavelet de-noising and de-trending techniques in order to mitigate these errors. Wavelet results indicate 80% improvement in reducing the RMS noise level compared to the results obtained with traditional Wiener low pass filtering.