Multi-step sensor management for localizing movable sources of spatially distributed phenomena

Localizing sources of physical quantities is often only possible in an indirect manner by observing the induced continuous phenomena, such as pollution loads of air or water. By employing model-based reconstruction methods, the task of localizing movable sources by distributed sensor measurements can be formulated as a non-linear stochastic parameter estimation problem. A computationally efficient state estimator is applied to this estimation problem for enabling real-time source localization. Furthermore, this paper proposes a novel approach to multistep sensor management for utilizing future sensors measurements in a most informative way. Here, predictive statistical linearization is employed for converting the given nonlinear non-Gaussian sensor management problem into a linear Gaussian one, which can be solved efficiently. By controlling a mobile sensor, it is demonstrated that the proposed method yields accurate source localization results.