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
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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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