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dc.contributor.authorFeng, J.
dc.contributor.authorQu, G.
dc.contributor.authorPotkonjak, M.
dc.date.accessioned2009-05-11T13:53:46Z
dc.date.available2009-05-11T13:53:46Z
dc.date.issued2004-10
dc.identifier.citationJ. Feng, G. Qu, and M. Potkonjak. "Sensor Calibration using Nonparametric Statistical Characterization of Error Models," 3rd IEEE Conference on Sensors (Sensors'2004), pp. 1456-1459, October 2004.en
dc.identifier.urihttp://hdl.handle.net/1903/9066
dc.description.abstractCalibration is the process of identifying and correcting for the systematic bias component of the error in sensor measurements. Traditionally, calibration has usually been conducted by considering a set of measurements in a single time frame and restricted to linear systems with the assumption of equal-quality sensors and single modality. The basis for the new calibration procedure is to construct a statistical error model that captures the characteristics of the measurement errors. Such an error model can be constructed either off-line or on-line. It is derived using the nonparametric kernel density estimation techniques. We propose four alternatives to make the transition from the constructed error model to the calibration model, which is represented by piecewise polynomials. In addition, statistical validation and evaluation methods such as resubstitution, is used in order to establish the interval of confidence for both the error model and the calibration model. Traces of the distance ranging measurements recorded by in-field deployed sensors are used as our demonstrative example.en
dc.format.extent541197 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectcalibrationen
dc.subjecterror modelen
dc.subjectcalibration modelen
dc.titleSENSOR CALIBRATION USING NONPARAMETRIC STATISTICAL CHARACTERIZATION OF ERROR MODELSen
dc.typeArticleen
dc.relation.isAvailableAtA. James Clark School of Engineeringen_us
dc.relation.isAvailableAtElectrical & Computer Engineeringen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.rights.licenseCopyright © 2004 IEEE. Reprinted from 3rd IEEE Conference on Sensors. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Maryland's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.


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