De-correlation adaptive niter for reducing the random drift at the output of FOG

Ya Nan Gao; Ting Peng Yang; Jia Bin Chen

De-correlation adaptive niter for reducing the random drift at the output of FOG

Ya Nan Gao^*, Ting Peng Yang, Jia Bin Chen

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The method that reduces the random drift in the fiber of gyro was studied. The feature of the noise in FOG was analyzed. De-correlation variable step size LMS algorithm was used to reduce the random drift at the FOG output. It ensures the faster convergence than conventional LMS algorithm. The experimental result shows that it is effective to improve the accuracy and SNR of FOG.

Original language	English
Pages (from-to)	476-478
Number of pages	3
Journal	Guangxue Jishu/Optical Technique
Volume	32
Issue number	3
Publication status	Published - May 2006

Keywords

De-correlation variable step size
Fiber of gyro
LMS
Random drift

Cite this

Gao, Y. N., Yang, T. P., & Chen, J. B. (2006). De-correlation adaptive niter for reducing the random drift at the output of FOG. Guangxue Jishu/Optical Technique, 32(3), 476-478.

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abstract = "The method that reduces the random drift in the fiber of gyro was studied. The feature of the noise in FOG was analyzed. De-correlation variable step size LMS algorithm was used to reduce the random drift at the FOG output. It ensures the faster convergence than conventional LMS algorithm. The experimental result shows that it is effective to improve the accuracy and SNR of FOG.",

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T1 - De-correlation adaptive niter for reducing the random drift at the output of FOG

AU - Gao, Ya Nan

AU - Yang, Ting Peng

AU - Chen, Jia Bin

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N2 - The method that reduces the random drift in the fiber of gyro was studied. The feature of the noise in FOG was analyzed. De-correlation variable step size LMS algorithm was used to reduce the random drift at the FOG output. It ensures the faster convergence than conventional LMS algorithm. The experimental result shows that it is effective to improve the accuracy and SNR of FOG.

AB - The method that reduces the random drift in the fiber of gyro was studied. The feature of the noise in FOG was analyzed. De-correlation variable step size LMS algorithm was used to reduce the random drift at the FOG output. It ensures the faster convergence than conventional LMS algorithm. The experimental result shows that it is effective to improve the accuracy and SNR of FOG.

KW - De-correlation variable step size

KW - Fiber of gyro

KW - LMS

KW - Random drift

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M3 - Article

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ER -

De-correlation adaptive niter for reducing the random drift at the output of FOG

Abstract

Keywords

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