Study on temperature error processing technique for fiber optic gyroscope

Fiber optic gyroscope (FOG) has been widely used as satellite and automobile attitude sensor for its high reliability and light weight. However, environment temperature variation results in errors, which will decrease the precision of FOG. The purpose of this paper is to present an error processing technique for FOG to reduce the influence of temperature variation based on the error analysis of FOG and a set of temperature experiment results. The technique contains two stages: one is de-noising stage, a novel de-noising algorithm is proposed, which integrated the advantages of lifting wavelet transform and adaptive forward linear prediction; the other is modeling stage, which based on Elman neural network. The simulation results show that the noise of FOG's output can be eliminated effectively by using the proposed de-noising algorithm, which is called LWT–FLP algorithm; the drift of FOG's output can be compensated effectively by Elman neural network model. The final processing results indicate that the errors caused by varied temperature have been reduced effectively by the proposed error processing technique.