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

^*此作品的通讯作者

自动化学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	476-478
页数	3
期刊	Guangxue Jishu/Optical Technique
卷	32
期	3
出版状态	已出版 - 5月 2006

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链接到 Scopus 的出版物

引用此

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

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