Study on error calibration of fiber optic gyroscope under intense ambient temperature variation.

A novel adaptive forward linear prediction (FLP) denoising algorithm and a temperature drift modeling and compensation concept based on ambient temperature change rate for fiber-optic gyroscope (FOG) are presented to calibrate the errors caused by intense ambient temperature variation. The intense ambient temperature variation will bring large temperature errors, which will degrade the performance of FOG. To analyze the temperature variation, characteristics of FOG temperature experiments are developed at first. Then the adaptive FLP denoising algorithm is employed to eliminate the noise aiming at reducing noise interference. After that, a simple modeling concept of building the compensation model between temperature drift and ambient temperature change rate is first to be given (we have not found a report of better results in any literature). The semiphysical simulation results show that the proposed method significantly reduces the noise and drift caused by intense ambient temperature variation.