Wind noise suppression using compact arrays of infrasound sensors

Abstract

This presentation describes how a compact array (aperture much less than the shortest wavelengths of interest) of infrasound sensors can be used to significantly enhance signal detection and waveform estimation in the presence of high levels of wind noise without the use of unwieldy mechanical screening. The methodology's effectiveness is founded on the fact that wind noise can be highly correlated on short spatiotemporal scales. This correlation structure is adaptively estimated from data and is used to formulate a generalized likelihood ratio detection problem and a minimum mean squared error waveform estimation problem. The infrasoudnic waveform is explicitly represented by a user-definable, parametrically characterized, stochastic prior distribution. Choice of this prior can enhance detection of anticipated signals and suppress others and thus more than one prior can be utilized in order to perform some level of classification. The presentation provides typical performance results from a range of application scenarios. Application to more traditional infrasound arrays is also presented, illustrating that exploitation of only the temporal correlation properties of wind noise is also beneficial in the context of the stochastic models and estimation techniques utilized.