The main objectives of this thesis would be to elaborate new detection and localization methods and validate and test them on both (controlled) real as well as simulated infrasound data. More precisely we will investigate the following items:
The use of high resolution methods like MUSIC (Multiple SIgnal Classification) to localize several narrow-band sources within the same time frequency cell.
Develop detection and localization methods for sources that are relatively wideband (i.e. may exist in several time frequency cells).
Develop new methods for the mitigation of spatially distributed (wide spread) interference sources.
Consider statistical criteria for the estimation of the number of sources via a penalized maximum likelihood approach.
Eventually, consider the use of learning methods for a better mitigation of certain ‘known’ ambient interference sources.
For the performance assessment and validation of the different methods under investigation, we will develop and enrich databases of controlled real-life data or synthetic data that would be representative of the genuine conditions and different scenarios for infrasound source detection. In particular, these data should well represent the diversity and variability of the infrasound signals and noise/interference sources as well as the different array configurations we might have in the current base stations.