MEMS (Micro-electromechanical Systems) gyroscope random error predication method based on grey wavelet neural network

A wavelet neural network and random error technology, applied in the direction of measuring devices, instruments, etc., can solve the problems of application limitations, low precision of MEMS gyro, etc., achieve good denoising effect, ensure accuracy, and reduce noise

Inactive Publication Date: 2014-07-02
HARBIN ENG UNIV
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Problems solved by technology

However, due to the lower precision of MEMS gyroscopes

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  • MEMS (Micro-electromechanical Systems) gyroscope random error predication method based on grey wavelet neural network
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  • MEMS (Micro-electromechanical Systems) gyroscope random error predication method based on grey wavelet neural network

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[0022] The method described in the present invention is a MEMS gyroscope random error prediction method. The invention adopts the prediction method of gray wavelet network. Compared with the traditional gyroscope random error modeling method, the method combines gray theory with wavelet neural network, Therefore, the prediction accuracy of MEMS gyroscope random error is improved, and compared with the traditional method, the prediction accuracy has been significantly improved.

[0023] to combine figure 1 , the technical solution of the present invention comprises the following steps:

[0024] Step 1: Preprocessing the output data of the MEMS gyroscope. The output data of the MEMS gyroscope is collected, the output data is analyzed by wavelet, and the Db4 wavelet function is selected to denoise the output data of the gyroscope.

[0025] First, install the inertial navigation system on the turntable, turn on the power and warm up for 15 minutes. Set up the serial port receiv...

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Abstract

The invention provides an MEMS (Micro-electromechanical Systems) gyroscope random error predication method based on a grey wavelet neural network. The predication method comprises the following steps: carrying out pretreatment on output data of an MEMS gyroscope, collecting the output data of the MEMS gyroscope, carrying out wavelet analysis on the output data, and selecting a Db4 wavelet function so as to carry out de-noising processing on the output data of the gyroscope; grouping the output data of the MEMS gyroscope after de-noising processing, and determining an input vector and a target vector; building a grey wavelet network predication model, determining the input node number of the grey wavelet network, the outputting node number and the hidden layer node number, and initializing the network; training the built network, and storing the network for predicating the trend of gyroscope random error. Compared with a traditional gyroscope random error modeling method, according to the method, a grey theory is combined with the wavelet neural network, and thus the predication accuracy of the MEMS gyroscope random error is improved, and the predication accuracy is obviously improved compared with the traditional method.

Description

technical field [0001] The invention relates to a random error prediction method of MEMS (micro mechanical gyroscope) in integrated navigation. Background technique [0002] The concept of MEMS was first proposed by Feyman, a famous American physicist. He pointed out that one of the problems in the development of MEMS technology is how to manufacture high-precision products with low-precision tools. MEMS gyroscopes have the following advantages: small size, small volume, light weight and relatively low cost; the processing technology used by MEMS gyroscopes is a silicon processing technology similar to integrated circuits, and the device size is small and light weight, suitable for mass production; performance Stable and strong anti-interference ability; relatively high reliability and easy integration, low power consumption. Due to these advantages of the MEMS gyroscope, it is widely used in many fields, especially aviation, aerospace, military and consumer fields. At pre...

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Application Information

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IPC IPC(8): G01C25/00
CPCG01C25/005
Inventor 沈锋兰晓明桑靖张金丽周阳迟晓彤韩浩刘向锋李伟东
Owner HARBIN ENG UNIV
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