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Evaluating Adaptive Filter Techniques for Wind Noise Removal from Infrasound Barometers

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Abstract
Infrasound signals, meaning those signals below 20 Hz, are commonly found throughout nature and man-made infrastructure. These signals can be sourced and analyzed using infrasound barometers or microphones. Often, infrasound signals carry very weak power due to their source being large distances away. Infrasound sensors can be very precise and are able to record these signals, however, in the presence of any external noise, these signals are easily lost. The most prominent source of noise in an outdoor sensing environment is wind, a non-stationary noise source. In this thesis, an ultrasonic anemometer is used to measure instantaneous wind speed at the same location to where an infrasound barometer takes measurements. This thesis will show that there is a weak correlation between the anemometer noise signal and the barometer signal. Normalized least-mean-square (NLMS) adaptive filtering will be used, specifically its residual error, as the noise-cancelled output from the barometer data. It will be shown that this method is successful at reducing a portion of the wind noise in the barometer signal, however, also that this method is not entirely successful in that it is not able to uncover lost infrasound signals due to noise. To evaluate this, a separate barometer with a passive wind filter will be deployed to serve as a comparison to an ideal condition. This thesis will also show that this is a challenging problem and outline the various solutions used to overcome challenges throughout the process.
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Thesis (Open Access)
Date
2024-09
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Attribution 4.0 International
License
http://creativecommons.org/licenses/by/4.0/
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