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Open AccessArticle

Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope

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Beijing Key Laboratory of Sensors, Beijing Information Science & Technology University, Beijing 100101, China
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Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, China
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Author to whom correspondence should be addressed.
Micromachines 2019, 10(9), 608; https://doi.org/10.3390/mi10090608
Received: 23 July 2019 / Revised: 8 September 2019 / Accepted: 12 September 2019 / Published: 13 September 2019
The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes. View Full-Text
Keywords: MEMS gyroscope; wavelet threshold de-noising; RBF neural network; inertial navigation system MEMS gyroscope; wavelet threshold de-noising; RBF neural network; inertial navigation system
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MDPI and ACS Style

Sheng, G.; Gao, G.; Zhang, B. Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope. Micromachines 2019, 10, 608.

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