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dc.contributorVirginia Tech. Department of Mechanical Engineeringen_US
dc.contributor.authorHoppe, Elizabeth A.en_US
dc.contributor.authorRoan, Michael J.en_US
dc.date.accessioned2015-05-27T19:50:19Z
dc.date.available2015-05-27T19:50:19Z
dc.date.issued2011-10-01
dc.identifier.citationHoppe, E. & Roan, M. (2011). Principal component analysis for emergent acoustic signal detection with supporting simulation results. Journal of the Acoustical Society of America, 130(4), 1962-1973. doi: 10.1121/1.3628324en_US
dc.identifier.issn0001-4966en_US
dc.identifier.urihttp://hdl.handle.net/10919/52654
dc.description.abstractA method is introduced that uses principal component analysis (PCA) to detect emergent acoustic signals. Emergent signal detection is frequently used in radar applications to detect signals of interest in background clutter and in cognitive radio to detect the primary user in a frequency band. The method presented differs from other standard techniques in that the detection of the signal of interest is accomplished by detecting a change in the covariance between two channels of data instead of detecting the change in statistics of a single channel of data. For this paper, PCA is able to detect emergent acoustic signals by detecting when there is a change in the eigenvalue subspace of the covariance matrix caused by the addition of the signal of interest. The algorithm's performance is compared to an energy detector and the Neyman-Pearson theorem. Acoustic simulations were used to verify the performance of the algorithm. Simulations were also used to examine the effectiveness of the algorithm under various signal-to-interferer and signal-to-noise ratios, and using various test signals.en_US
dc.format.extent12 pagesen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.publisherAcoustical Society of Americaen_US
dc.subjectEigenvaluesen_US
dc.subjectSubspacesen_US
dc.subjectAcoustic noiseen_US
dc.subjectAcoustic sensingen_US
dc.subjectAutomatic speech recognition systemsen_US
dc.titlePrincipal component analysis for emergent acoustic signal detection with supporting simulation resultsen_US
dc.typeArticle - Refereeden_US
dc.identifier.urlhttp://scitation.aip.org/content/asa/journal/jasa/130/4/10.1121/1.3628324en_US
dc.date.accessed2015-05-12en_US
dc.title.serialJournal of the Acoustical Society of Americaen_US
dc.identifier.doihttps://doi.org/10.1121/1.3628324
dc.type.dcmitypeTexten_US


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