Joint estimation decision methods for source localization and restoration in parametric convolution processes. Application to accidental pollutant release
In this paper, the problem of pollutant source localization and flow estimation is addressed. Potential applications of this work include leakage of hazardous chemicals or industrial effluents coming from an accidental situation. It is tackled in a one-dimensional context such as river, tunnel, canal with the aid of a single remote sensor. The pollutant is assumed to be coming from one out of N possible sources. Measurements are the result of a parametric convolution integral. The task may be viewed as a conditional deconvolution which requires a priori knowledge. In order to reduce the set of solutions, a source flow model is considered which introduces time bounds of the accidental spill. A joint estimation decision is derived in a Bayesian framework in both cases: with and without source assumptions. Without source model, the algorithm is unable to recover far sources location. On the contrary, the proposed source model enables to balance decision and take into account near and far sources as well. The benefit for this kind of solution is shown practically in terms of localization quality.
localization pollution deconvolution inverse problems bayesian methods