ARMA模型辨识及其在光纤陀螺漂移建模中的应用

针对含有噪声的时间序列模型辨识精度低的问题,采用自回归函数的衰减正弦曲线方法估计含有噪声的ARMA模型系统参数,AR参数从衰减正弦曲线直接获得,MA参数估计采用相关匹配技术.仿真结果表明,时于具有不同信噪比噪声干扰的系统,该算法具有良好的收敛性和准确性.在此基础上,将该方法应用到光纤陀螺漂移误差建模中,实验结果表明,采用该算法所确定的模型能够精确的反应光纤陀螺的漂移趋势.

Identification with ARMA model and its application to modeling of FOG drift

Aiming at question that low identification precision of time series model system in noise,the ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise ARMA signal.The AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters with guaranteed stability.The MA parameters are estimated using a correlation matching technique.The simulation results show that with this method only less calculation work is needed and good convergence and accuracy can be achieved in various signal-to-noise systems.This method can be successfully applied to drift error modeling of fiber optic gyroscope.The experiment result shows model established can reflect gyro drift tendency with reasonable accuracy.