传统的系统级标定方法是将温箱压缩机、转台电机等环境引起的随机噪声视为白噪声，而实际过程中输出信号具有非平稳和非线性特征。最小二乘拟合法是一种线性回归方法，利用该方法求解的误差系数成为制约光纤捷联惯导系统实现高精度的关键因素之一。针对上述问题，该文提出了一种基于希尔伯特-黄变换的捷联惯导系统级标定改进算法。通过希尔伯特-黄变换去除惯性仪表原始输出信号中的高频随机噪声，再利用系统级标定法计算惯性测量单元(IMU)误差参数。经多组试验证明，该方法能够有效提高IMU误差参数辨识的准确性，1 h的动态导航位置误差相对减少约10%。

The traditional system-level calibration method regards the random noise caused by the thermostat compressor, turntable motor and other environments as white noise, but the output signal in the actual process has non-stationary and nonlinear characteristics, while the least squares fitting method is a linear regression, the error coefficient solved by this method becomes one of the key factors restricting the high precision of the fiber optic strap-down inertial navigation system. Aiming at the above-mentioned problems, an improved SINS system-level calibration algorithm based on Hilbert-Huang transform is proposed. The high-frequency random noise in the original output signal of the inertial instrument is removed based on Hilbert-Huang transform, and then the IMU error parameters are calculated by the system-level calibration improved method. Through multiple sets of experiments, it has been proved that this method can effectively improve the accuracy of IMU error parameter identification, and the dynamic navigation position error of 1 h is relatively reduced by about 10%.