Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion

Abstract

The precise localization of the infrasound source is important for infrasound event monitoring. The localization of infrasound sources is influenced by the atmospheric propagation environment and infrasound measurement equipment in the large-scale global distribution of infrasound arrays. A distributed infrasound source localization method based on sparse Bayesian learning (SBL) and Bayesian information fusion is proposed to reduce the localization error. First, the arrival azimuth of the infrasound source is obtained based on the SBL algorithm. Then, the infrasound source localization result is obtained by the Bayesian information fusion algorithm. The localization error of the infrasound source can be reduced by this infrasound source method, which incorporates the uncertainty of the infrasound propagation environment and infrasound measurement equipment into the infrasound source localization results. The effectiveness of the proposed algorithm was validated using rocket motor explosion data from the Utah Test and Training Range (UTTR). The experimental results show that the arrival azimuth estimation error can be within 2° and the localization distance error is 3.5 km.