A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors
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TLDR
This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors and discusses on the current limitations of the state of the art and recommendations for future research.About:
This article is published in Information Fusion.The article was published on 2017-07-01 and is currently open access. It has received 202 citations till now. The article focuses on the topics: Atmospheric dispersion modeling.read more
Citations
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Journal ArticleDOI
Information-Based Search for an Atmospheric Release Using a Mobile Robot: Algorithm and Experiments
TL;DR: The first experimental result of a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately, is presented.
Journal ArticleDOI
Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions
TL;DR: This paper compares the performance and search behaviour of Entrotaxis with the popular Infotaxis algorithm, for searching in sparse and turbulent conditions where typical gradient-based approaches become inefficient or fail, and achieves a faster mean search time.
Journal ArticleDOI
Natural gas fugitive leak detection using an unmanned aerial vehicle: Localization and quantification of emission rate
Levi M. Golston,Nicholas F. Aubut,Michael B. Frish,Shuting Yang,Robert W. Talbot,Christopher Gretencord,James McSpiritt,Mark A. Zondlo +7 more
TL;DR: In this paper, a set of methods for locating and quantifying natural gas leaks using a small unmanned aerial system equipped with a pathintegrated methane sensor is described, supported by a series of over 200 methane release trials covering 51 release location and flow rate combinations.
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Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization
TL;DR: A fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM) that can effectively accelerate the process of convergence.
Journal ArticleDOI
Bayesian source term estimation of atmospheric releases in urban areas using LES approach.
TL;DR: A novel source term estimation method is proposed based on LES approach using Bayesian inference that reduces the errors of source location and releasing strength by 77% and 28%, respectively.
References
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Bayesian Inference for Source Term Estimation: Application to the International Monitoring System Radionuclide Network
TL;DR: In this paper, the authors demonstrate that a rigorous and general framework for addressing these problems is through Bayesian probability theory, allowing the rational inference of the posterior probability distribution of the source parameters of interest given any prior information and available activity concentration measurements.
Proceedings ArticleDOI
Multi UAV negotiation for coordinated tracking of contaminant cloud
TL;DR: A mechanism to detect and model the shape of a contaminant cloud boundary using air borne sensor swarms and a negotiation mechanism is developed for the allocation of the regions of exploration to the UAVs.
Proceedings ArticleDOI
Biological Source Term Estimation Using Particle Counters and Immunoassay Sensors
TL;DR: Two simple probabilistic sensor models for detection of airborne biological agents are described and their combined implementation into a biological release source term estimator and subsequent promising performance in a simulated release of hazardous airborne biological material are demonstrated.