A fast algorithm for the high order linear and nonlinear Gaussian regression filter

W. Zeng; X. Jiang; P. J. Scott; S. Xiao; L. Blunt

A fast algorithm for the high order linear and nonlinear Gaussian regression filter

W. Zeng, X. Jiang, P. J. Scott, S. Xiao, L. Blunt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 9th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2009
Publisher	euspen
Pages	356-359
Number of pages	4
Volume	2
ISBN (Electronic)	9780955308260
Publication status	Published - 2009
Event	9th International Conference of the European Society for Precision Engineering and Nanotechnology - San Sebastian, Spain Duration: 2 Jun 2009 → 5 Jun 2009 Conference number: 9

Conference

Conference	9th International Conference of the European Society for Precision Engineering and Nanotechnology
Abbreviated title	EUSPEN 2009
Country/Territory	Spain
City	San Sebastian
Period	2/06/09 → 5/06/09

Cite this

@inproceedings{02d2c49034404c719a7dfe9aca588660,

title = "A fast algorithm for the high order linear and nonlinear Gaussian regression filter",

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.",

author = "W. Zeng and X. Jiang and Scott, {P. J.} and S. Xiao and L. Blunt",

year = "2009",

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booktitle = "Proceedings of the 9th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2009",

publisher = "euspen",

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note = "9th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2009 ; Conference date: 02-06-2009 Through 05-06-2009",

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Zeng, W , Jiang, X , Scott, PJ, Xiao, S & Blunt, L 2009, A fast algorithm for the high order linear and nonlinear Gaussian regression filter. in Proceedings of the 9th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2009. vol. 2, euspen, pp. 356-359, 9th International Conference of the European Society for Precision Engineering and Nanotechnology, San Sebastian, Spain, 2/06/09.

A fast algorithm for the high order linear and nonlinear Gaussian regression filter. / Zeng, W.; Jiang, X.; Scott, P. J. et al.

Proceedings of the 9th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2009. Vol. 2 euspen, 2009. p. 356-359.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A fast algorithm for the high order linear and nonlinear Gaussian regression filter

AU - Zeng, W.

AU - Jiang, X.

AU - Scott, P. J.

AU - Xiao, S.

AU - Blunt, L.

N1 - Conference code: 9

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

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VL - 2

SP - 356

EP - 359

BT - Proceedings of the 9th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2009

PB - euspen

T2 - 9th International Conference of the European Society for Precision Engineering and Nanotechnology

Y2 - 2 June 2009 through 5 June 2009

ER -

A fast algorithm for the high order linear and nonlinear Gaussian regression filter

Abstract

Conference

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