The temperature error modeling and compensation method of fiber optic gyroscope scaling factor

Abstract

Fiber optic gyroscope (FOG) has the advantages of small size, light weight, large dynamic range, fast start up and long life. It can be widely used in military fields and civil fields. As the performance of the optoelectronic devices in the FOG varies with the temperature, the performance of the FOG will be affected. In the use of the carrier high speed, the scaling factor error caused by temperature change is the main error of the FOG, its effect on accuracy is much greater than random drift. FOG often needs to work in a wide temperature range, so the scaling factor needs to be modeled and compensated. Because of the temperature error of the scaling factor is very non-linear, the accuracy of using the traditional polynomial fitting method to compensate the scaling factor is poor. The neural network can approximate any continuous function with any desired accuracy, so this paper uses the BP neural network method to compensate the temperature error of the FOG scaling factor. First, this paper analyzes the error mechanism of the scaling factor, and establishes a theoretical model of the temperature error of the scaling factor. Then the FOG scaling factor in the full temperature range is measured, and the temperature error of the scaling factor is modeled and compensated by using the above two methods. It can be seen from the compensation results that the neural network model can get a good compensation effect, and the accuracy is better than the polynomial fitting method.