Advances in statistical signal processing for infrasound events

Abstract : The core of this thesis is the infrasound signal processing and particularly the estimation and detection using a sensor array. The themes presented here are well-suited to experimentation and we tried, as much as possible, to illustrate the theoretical results with real data. The first part describes the implications and difficulties of infrasonic detection. We review the state-of-the-art of detection techniques based on hypothesis testing and supplement them with the aim of automatic detection. The second part of this thesis highlights the error on the angle of arrival estimation due to considering the arrays as planar (common approximation). In response to this problem, a new estimator considering the full geometry of the array and including an a priori on the speed of the infrasonic wave is derived and studied. We show that the described estimator significantly improve performance. The third part introduces the modeling of infrasonic signal as autoregressive process. This modeling allows us to derived a sequential approach to detect infrasound based on the beamforming and the detection of abrupt changes. The last part of this manuscript aims to propose an alternative "signal'' model. We present new results highlighting a phenomenon of loss of coherence of the signals recorded by different sensors. This work leads to the generation of synthetic infrasonic signal in order to study the performance of the detection algorithms.
Keywords : Infrasound
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Adrien Nouvellet. Advances in statistical signal processing for infrasound events. Signal and Image processing. Télécom ParisTech, 2016. English. ⟨NNT : 2016ENST0011⟩. ⟨tel-01492873⟩

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