Temperature compensation of FOG scale factor based on CPSO-BPNN

The scale factor of fiber optic gyroscope (FOG) varied with the environment temperature. This nonlinear variation seriously influences the precision of the FOG. In this article, the back propagation neural network (BPNN) based on chaos particle swarm optimization (CPSO) is used to compensate the scale factor error. It is testified by experiment, that CPSO-BPNN algorithm is an ideal method to fit the variation of scale factor with temperature, which can greatly decrease the angular rate error of FOG caused by scale factor error and guarantee the measuring precision of FOG at different temperature.