Detecting changes in signals and systems—a survey
Automatica (Journal of IFAC)
Bayesian deconvolution of Bernoulli-Gaussian processes
Signal Processing
Sparse deconvolution using adaptive mixed-Gaussian models
Signal Processing
Improved Methods of Maximum a Posteriori Restoration
IEEE Transactions on Computers
Detection and localization of vapor-emitting sources
IEEE Transactions on Signal Processing
Deconvolution of sparse spike trains by iterated windowmaximization
IEEE Transactions on Signal Processing
A Sequential Detector for Biochemical Release in Realistic Environments
IEEE Transactions on Signal Processing
Landmine detection and localization using chemical sensor arrayprocessing
IEEE Transactions on Signal Processing
Biochemical Transport Modeling and Bayesian Source Estimation in Realistic Environments
IEEE Transactions on Signal Processing
Maximum likelihood detection and estimation of Bernoulli - Gaussian processes
IEEE Transactions on Information Theory
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
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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.