Adaptive Wavelet Neural Network Denoising Modeling Method Based on Forward Linear Prediction

Abstract

The invention relates to an adaptive wavelet neural network denoising modeling method based on forward linear prediction, comprising the following steps: (1): using wavelet transform to perform multi-scale decomposition on the zero-drift signal of the fiber optic gyro at the current moment; (2) : Perform single-branch reconstruction on the decomposed approximate signal and detail signal to obtain the reconstructed approximate signal and detail signal; (3): Use the FLP method for the approximate signal and detail signal in step (2) layer by layer. Denoising; (4): Use the layer-by-layer denoised signal obtained in step (3) as the input of the neural network, and use the fiber optic gyro signal at the next moment as the output to train the network, and the training is completed. Denoising and modeling of fiber optic gyroscope zero-drift signal. The method of the invention integrates denoising and modeling, effectively improves the modeling and compensation accuracy of the zero-drift signal of the fiber optic gyro, and is easy to implement.

Description

technical field [0001] The invention belongs to signal processing in the field of inertial technology, and relates to a method for processing a zero-drift signal of an optical fiber gyroscope, in particular to an adaptive wavelet neural network denoising modeling method based on forward linear prediction (FLP)-FLP-WNN algorithm , applicable to various fiber optic gyroscopes. Background technique [0002] Fiber optic gyroscope (FOG) has been greatly developed since the principle scheme was proposed in 1976. The fiber optic gyroscope is a new type of all-solid-state gyroscope developed on the basis of the Sagnac effect. It is an inertial measurement element without mechanical rotating parts, but because its output signal is often affected by noise, it will cause drift and affect the performance of the gyroscope. output characteristics. Therefore, how to remove the noise and compensate the drift to improve the zero-drift performance of the signal is a very important topic in ...

AI Technical Summary

Problems solved by technology

[0005] Technical problem solving of the present invention: overcome the deficiencies in the prior art, a kind of adaptive wavelet neural network denoising modeling method-FLP-WNN algorithm based on forward linear prediction (FLP), this method combines wavelet transform, FLP algorithm and The advantages of the neural network algorithm are combined, which can effectively remove the noise in the fiber optic gyroscope signal, improve the modeling accuracy, and be easy to program

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