Improving Separation of Signals from Multiple Physical Quantities Detected by Sensor Arrays

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dc.contributor.authorMorgan, Sarah Elizabethen
dc.contributor.committeechairMartin, Eileen R.en
dc.contributor.committeememberGugercin, Serkanen
dc.contributor.committeememberPickrell, Gary R.en
dc.contributor.departmentMathematicsen
dc.date.accessioned2022-06-01T08:00:38Zen
dc.date.available2022-06-01T08:00:38Zen
dc.date.issued2022-05-31en
dc.description.abstractModern array sensing systems, such as distributed fiber optic sensing, are used in many applications which may record a mixture of responses to multiple physical quantities. In these applications, it may be helpful to be able to separate this mixture of responses into the signals resulting from the individual sources. This is similar to the cocktail party problem posed with Independent Component Analysis (ICA), in which we use gradient ascent and fixed point iteration optimization algorithms to achieve this separation. We then seek to apply the problem setup from ICA to mixed signals resulting from a sensor array with the goal of maintaining coherence throughout resulting spatial arrays. We propose a new post-processing technique after separation to pair up the signals from different types of physical quantities based on the Symmetric Reverse Cuthill-McKee (SRCM) and Symmetric Approximate Minimum Degree (SAMD) permutations of the coherence matrix.en
dc.description.abstractgeneralSome modern sensing systems are able to collect data resulting from different types of sources, such as vibrations and electromagnetic waves, at the same time. This means we have signals resulting from a mixture of sources. An example of one such modern sensing system is distributed fiber optic sensors used in geoscience applications, such as seismology and subsurface imaging, which measures strain along the fiber optic cable. In many applications, it may be helpful to obtain the signals from each of these sources separately, instead of having a mixture of these sources. We propose the use of optimization algorithms, in particular two algorithms arising from Independent Component Analysis (ICA), which seek to maximize a function in order to separate these signals. We then explore changes required to the algorithms for scenarios in which we have multiple sensors spaced some distance away from each other which record signals from two different sources. We also present a method of determining which separated signals correspond to which sensors after performing signal separation.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:34910en
dc.identifier.urihttp://hdl.handle.net/10919/110376en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectApplied Mathen
dc.subjectSignal Processingen
dc.subjectTime Series Analysisen
dc.titleImproving Separation of Signals from Multiple Physical Quantities Detected by Sensor Arraysen
dc.typeThesisen
thesis.degree.disciplineMathematicsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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