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A Fast Algorithm for the High Order Linear and Nonlinear Gaussian Regression filter

Zeng, Wenhan, Jiang, Xiang, Scott, Paul J., Xiao, Shaojun and Blunt, Liam (2009) A Fast Algorithm for the High Order Linear and Nonlinear Gaussian Regression filter. In: Proceedings of the 9 th international conference of the european society for precision engineering and nanotechnology. euspen, San Sebastian, Spain, pp. 356-359. ISBN 978-0-9553082-6-0

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    Abstract

    In this paper, the general model of the Gaussian regression filter, including both the linear and nonlinear filter of zeroth, second order, has been reviewed. A fast algorithm based on the FFT algorithm has been proposed and tested for its speed and accuracy. Both simulated and practical engineering data have been used in the testing of the proposed algorithm. Results show that with the same accuracy, the processing times of the second order linear and nonlinear regression filters for a typical 40,000 points dataset have been reduced to under 0.5second from the several hours of the traditional convolution algorithm.

    Item Type: Book Chapter
    Subjects: T Technology > TS Manufactures
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Centre for Precision Technologies
    School of Computing and Engineering > Centre for Precision Technologies > Surface Metrology Group
    Related URLs:
    Depositing User: Wenhan Zeng
    Date Deposited: 27 Apr 2009 12:17
    Last Modified: 21 Nov 2013 14:05
    URI: http://eprints.hud.ac.uk/id/eprint/3980

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