Method and apparatus for signal detection- classification and estimation from compressive measurements

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dc.contributor.assigneeRice Universityen_US
dc.contributor.publisherUnited States Patent and Trademark Officeen_US
dc.creatorBaraniuk, Richard G.en_US
dc.creatorDuarte, Marco F.en_US
dc.creatorDavenport, Mark A.en_US
dc.creatorWakin, Michael B.en_US
dc.date.accessioned2015-05-04T19:06:04Zen_US
dc.date.available2015-05-04T19:06:04Zen_US
dc.date.filed2006-10-25en_US
dc.date.issued2013-07-09en_US
dc.description.abstractThe recently introduced theory of Compressive Sensing (CS) enables a new method for signal recovery from incomplete information (a reduced set of “compressive” linear measurements), based on the assumption that the signal is sparse in some dictionary. Such compressive measurement schemes are desirable in practice for reducing the costs of signal acquisition, storage, and processing. However, the current CS framework considers only a certain task (signal recovery) and only in a certain model setting (sparsity). We show that compressive measurements are in fact information scalable, allowing one to answer a broad spectrum of questions about a signal when provided only with a reduced set of compressive measurements. These questions range from complete signal recovery at one extreme down to a simple binary detection decision at the other. (Questions in between include, for example, estimation and classification.) We provide techniques such as a “compressive matched filter” for answering several of these questions given the available measurements, often without needing to first reconstruct the signal. In many cases, these techniques can succeed with far fewer measurements than would be required for full signal recovery, and such techniques can also be computationally more efficient. Based on additional mathematical insight, we discuss information scalable algorithms in several model settings, including sparsity (as in CS), but also in parametric or manifold-based settings and in model-free settings for generic statements of detection, classification, and estimation problems.en_US
dc.digitization.specificationsThis patent information was downloaded from the US Patent and Trademark website (http://www.uspto.gov/) as image-PDFs. The PDFs were OCRed for access purposes.en_US
dc.format.extent27 ppen_US
dc.identifier.citationBaraniuk, Richard G., Duarte, Marco F., Davenport, Mark A. and Wakin, Michael B., "Method and apparatus for signal detection- classification and estimation from compressive measurements." Patent US8483492B2. issued 2013-07-09. Retrieved from https://hdl.handle.net/1911/80128.en_US
dc.identifier.patentIDUS8483492B2en_US
dc.identifier.urihttps://hdl.handle.net/1911/80128en_US
dc.language.isoengen_US
dc.titleMethod and apparatus for signal detection- classification and estimation from compressive measurementsen_US
dc.typeUtility patenten_US
dc.type.dcmiTexten_US
dc.type.genrepatentsen_US
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