FOG temperature drift compensation method based on wavelet denoising and neural network

Abstract

The output of fiber optic gyroscope (FOG) is easily affected by the temperature variations, so it leads to produce drift and the measurement accuracy of FOG is reduced. The traditional BP neural network is an optimization method of local search, which is easy to fall into local minimum, leading to the failure of network training. In order to optimize BP neural network, a temperature drift compensation method for FOG based on particle swarm optimization (PSO) and wavelet denoising is proposed. Firstly, the mechanism of FOG temperature drift is analyzed. Next, FOG static state test in different temperatures is finished. Finally, the FOG temperature drift model has been built by the method and compensate. The results show that the output standard deviation of FOG at different temperatures is reduced by 60.19%, and the compensation effect is better than traditional BP neural network.