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
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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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TL;DR: In this paper, the identification of multiple point sources, emitting the same tracer, from their limited set of merged concentration measurements is addressed, referring to the estimation of locations and strengths of a known number of simultaneous point releases.
Proceedings ArticleDOI
Non-linear Bayesian CBRN source term estimation
P. Robins,P. Thomas +1 more
TL;DR: A Bayesian posterior probability density sampling algorithm suitable for use in estimating chemical, biological, radiological or nuclear atmospheric releases of material is proposed, demonstrating that the sampling scheme performs adequately despite constraints of a short time span to calculate inferences from complex likelihood calculations.
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Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique
TL;DR: It is shown that the discrete renormalized solution belongs to a family of well-known inverse solutions (minimum weighted norm solutions), which can be computed by using the concept of generalized inverse operator, and it is demonstrated that this operator satisfies the criteria used in geophysics to define good inverses.
Proceedings ArticleDOI
Decentralized multi-agent tracking of unknown environmental level sets by a team of nonholonomic robots
TL;DR: A new distributed navigation and guidance strategy is presented that ensures convergence of all robots to the desired level set and its subsequent display via stable circulation of the robots along this set and the effectiveness of the proposed guidance law is confirmed by computer simulations and real-world experiments.
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Boundary tracking and estimation of pollutant plumes with a mobile sensor in a low-density static sensor network
TL;DR: A heuristic method for tracking and estimating the boundary of a dynamic plume with a mobile sensor in addition to binary below/above threshold signals from a low-density static sensor network is proposed, which is efficient in terms of sensor movement.