Invention Grant
- Patent Title: Method and apparatus for signal detection, classification and estimation from compressive measurements
- Patent Title (中): 用于信号检测,分类和压缩测量估计的方法和装置
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Application No.: US12091069Application Date: 2006-10-25
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Publication No.: US08483492B2Publication Date: 2013-07-09
- Inventor: Richard Baraniuk , Marco F. Duarte , Mark A. Davenport , Michael B. Wakin
- Applicant: Richard Baraniuk , Marco F. Duarte , Mark A. Davenport , Michael B. Wakin
- Applicant Address: US TX Houston
- Assignee: William Marsh Rice University
- Current Assignee: William Marsh Rice University
- Current Assignee Address: US TX Houston
- Agency: 24IP Law Group
- Agent Timothy R. DeWitt
- International Application: PCT/US2006/041454 WO 20061025
- International Announcement: WO2007/050593 WO 20070503
- Main IPC: G06K9/36
- IPC: G06K9/36 ; G06K9/46

Abstract:
The 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.
Public/Granted literature
- US20080228446A1 Method and Apparatus for Signal Detection, Classification and Estimation from Compressive Measurements Public/Granted day:2008-09-18
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