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John W. Fisher, III. Dean, Christopher L. (Christopher Lee) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2015-11-09T19:13:20Z 2015-11-09T19:13:20Z 2015 2015 http://hdl.handle.net/1721.1/99790 928028048 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 103-105). A common challenge in environmental impact studies and frontier exploration is identifying the properties of some emitters from remotely obtained concentration data. For example, consider estimating the volume of some pollutant that a chemical refinery releases into the atmosphere from measurements of pollutant concentrations. Previous methods assume a known number of emitters, low ambient concentrations, or measurements from a group of stationary sensors. In contrast, we use measurements from a mobile sensor and detect source contributions that are several orders of magnitude smaller than ambient concentrations. Here, we develop and analyze a method for inferring the location, emission rate, and number of emitters from measurements taken by an aircraft. We use Reversible-jump Markov chain Monte Carlo sampling to jointly infer the posterior distribution of the number of emitters and emitter properties. Additionally, we develop performance metrics that can be efficiently computed using the sample-based representation of the posterior distribution. We investigate the expected performance of the inference algorithm with respect to certain model parameters in a series of synthetic experiments and use these performance metrics for evaluation. These experiments provide insight into subtleties of the model, including the identifiability of source configurations, the effect of various path geometries, and the effects of incorporating data from multiple flights. We also provide intuition for best-case performance when running on real-world data using a synthetic experiment. Finally, we demonstrate our ability to process and analyze real-world data for which the true source configuration is unknown. by Christopher L. Dean. S.M. 105 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Efficient MCMC inference for remote sensing of emission sources Efficient Markov chain Monte Carlo inference for remote sensing of emission sources Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ce41ecaf-4de5-4101-9b90-c8417accb891"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Efficient MCMC inference for remote sensing of emission sources</Title> <Subtitle>Efficient Markov chain Monte Carlo inference for remote sensing of emission sources</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Dean, Christopher L. (Christopher Lee)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>A common challenge in environmental impact studies and frontier exploration is identifying the properties of some emitters from remotely obtained concentration data. For example, consider estimating the volume of some pollutant that a chemical refinery releases into the atmosphere from measurements of pollutant concentrations. Previous methods assume a known number of emitters, low ambient concentrations, or measurements from a group of stationary sensors. In contrast, we use measurements from a mobile sensor and detect source contributions that are several orders of magnitude smaller than ambient concentrations. Here, we develop and analyze a method for inferring the location, emission rate, and number of emitters from measurements taken by an aircraft. We use Reversible-jump Markov chain Monte Carlo sampling to jointly infer the posterior distribution of the number of emitters and emitter properties. Additionally, we develop performance metrics that can be efficiently computed using the sample-based representation of the posterior distribution. We investigate the expected performance of the inference algorithm with respect to certain model parameters in a series of synthetic experiments and use these performance metrics for evaluation. These experiments provide insight into subtleties of the model, including the identifiability of source configurations, the effect of various path geometries, and the effects of incorporating data from multiple flights. We also provide intuition for best-case performance when running on real-world data using a synthetic experiment. Finally, we demonstrate our ability to process and analyze real-world data for which the true source configuration is unknown.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>