Real-time data quality assessment using linear prediction Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/4j03d196r

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  • The U.S. Air Force models many naturally occurring phenomena. To validate such models, an automated data collection system is used. Since validation is dependent upon correct data, an inexpensive method to detect erroneous data was investigated. The method uses a Wiener filter to predict values one sample in advance. If the predicted value differs from the measured value by some constant, then erroneous data are detected. When applied to the meteorological parameter "temperature", a sixth-order filter was designed that would flag good data as correct 97.64% of the time with an error flag set at three standard deviations. This thesis outlines the software requirements, design, and testing necessary to use this method.
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