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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.