Application and compensation for startup phase of FOG based on RBF neural network

Fiber optic gyroscope (FOG) is sensitive to temperature, and there is a certain temperature drift error in the working process of FOG especially in the startup phase. In this paper, to reduce the bias drift in the startup phase of FOG and shorten the startup time, a scheme based on radial basis function (RBF) neural networks was designed to compensate the drift in the startup phase of FOG. The model took the temperature of FOG and the temperature change rate as the inputs and used the bias drift of FOG as the output. In the room temperature, the RBF neural network was used to compensate the startup drift of FOG, and the experiment shows that the method can effectively reduce the temperature drift and shorten the startup time of FOG. This method is used in a certain type of FOG north finder and can greatly reduce the preparation time, and so improves the north-seeking accuracy.