On-line detection and estimation of gaseous point sources using sensor networks

The current work tackles the detection and localization of a diffusive point source, based on spatially distributed concentration measurements acquired through a sensor network. A model-based strategy is used, where the concentration field is modeled as a diffusive and advective-diffusive semi-infinite environment. We rely on hypothesis testing for source detection and maximum likelihood estimation for inference of the unknown parameters, providing Cramér-Rao Lower Bounds as benchmark. The (non-convex and multimodal) likelihood function is maximized through a Newton-Conjugate Gradient method, with an applied convex relaxation under steady-state assumptions to provide a suitable source position initialization. Detection is carried out resorting to a Generalized Likelihood Ratio Test. The framework's robustness is validated against a numerically simulated environment generated by the Toolbox of Level Set Methods, which provides data (loosely) consistent with the model.