Startup compensation for fiber optic gyro based on RBF neural networks

摘要

Based on the excellent performance of the RBF neural networks, the RBF neural networks are used in the startup compensation of high-precision FOG. In this paper, the startup of the high-precision FOG is analyzed in detail. Actual experimentation results demonstrate that: after the compensation, the bias stability of the gyro can reaches the nominal accuracy; the bias repeatability of the gyro can be improved dramatically. Therefore, this method proposed in this paper has an important value in practical application.