Using the Matérn covariance function to enhance detection and estimation of transient infrasound

Abstract

Adaptive filtering techniques are well known to enhance detection of anomolous events in the presence of slowly changing (quasi-stationary), autocorrelated noise backgrounds such as those occurring in measurements of infrasound signals in the atmosphere. The noise in this case is dominated by the effect of the turbulent wind blowing over the sensing element. While mechanical windscreens can provide significant signal-to-noise ratio gains, additional benefits can be obtained by employing adaptive filters. In this presentation, a kernel-based adaptive filter based on the Matérn Covariance Function is demonstrated to improve the probability of detection of transient infrasound signals as well as improve signal estimation errors without increasing false alarm rates in the presence of wind noise. The choice of the Matérn Covaraiance Function to represent the wind noise process is motivated by its roots in fractional-order stochastic differential equations. Because the wind noise at infrasound frequencies can be influenced by shear-turbulence interaction and turbulence-turbulence interaction more than one kernel is required to obtain optimal performance for the filter. Results are presented under a range wind noise conditions.