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dc.contributor.advisor Collins, Leslie M en_US
dc.contributor.author Morton, Kenneth D. en_US
dc.date.accessioned 2010-05-10T20:18:50Z
dc.date.available 2012-05-01T04:30:05Z
dc.date.issued 2010 en_US
dc.identifier.uri http://hdl.handle.net/10161/2477
dc.description Dissertation en_US
dc.description.abstract <p>Automated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing acoustic sensing tasks with ease in a variety of situations, the performance of current automated acoustic sensing algorithms is limited by seemingly benign changes in environmental or operating conditions. In this work, a framework for acoustic surveillance that is capable of accounting for changing environmental and operational conditions, is developed and analyzed. The algorithms employed in this work utilize non-stationary and nonparametric Bayesian inference techniques to allow the resulting framework to adapt to varying background signals and allow the system to characterize new signals of interest when additional information is available. The performance of each of the two stages of the framework is compared to existing techniques and superior performance of the proposed methodology is demonstrated. The algorithms developed operate on the time-domain acoustic signals in a nonparametric manner, thus enabling them to operate on other types of time-series data without the need to perform application specific tuning. This is demonstrated in this work as the developed models are successfully applied, without alteration, to landmine signatures resulting from ground penetrating radar data. The nonparametric statistical models developed in this work for the characterization of acoustic signals may ultimately be useful not only in acoustic surveillance but also other topics within acoustic sensing.</p> en_US
dc.format.extent 5340573 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Engineering, Electronics and Electrical en_US
dc.subject Acoustic en_US
dc.subject Autoregressive en_US
dc.subject Bayesian en_US
dc.subject Nonparametric en_US
dc.title Bayesian Techniques for Adaptive Acoustic Surveillance en_US
dc.type Dissertation en_US
dc.department Electrical and Computer Engineering en_US
duke.embargo.months 24 en_US

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