Detection and Localization of Material Releases With Sparse Sensor Configurations

We consider the problem of detecting and localizing a material release utilizing sparse sensor measurements and formulate the problem as one of abrupt change detection. Methods which rely on single-sensor detection require dense deployment to achieve adequate coverage; costly sensors preclude such approaches. Furthermore, localization requires the fusion of multiple sensor measurements. Fusion in sparse sensor configurations is dependent on the knowledge of the dynamics of particle dispersion, which is, itself, problematic due to the inherent randomness on the wind field. We consider the efficacy of using an approximate dynamic model with coarse parameter estimates for the detection and localization of material releases. Specifically, we consider propagation models consisting of diffusion plus transport according to a Gaussian dispersion model. Assuming a known wind field, unconstrained intersensor communication, and a centralized processor, we derive optimal inference algorithms and provide a hybrid detection-localization hypothesis-testing framework with linear growth in the hypothesis space. We then analyze the probability of detection, time-to-detection, and localization performance as a function of the number of sensors. Furthermore, we examine the impact on performance when the underlying dynamical model deviates from the assumed model. This detailed analysis provides the basis for the design of more sophisticated algorithms for 1) performing robust detection followed by refined nonlinear parameter estimation which provides enhanced localization, and 2) distributed architectures aimed at conserving communication resources in which detections within local clusters are used to trigger more intensive intercluster communication to improve detection and localization