光纤陀螺(FOG)的漂移输出经常淹没在噪声中，直接建模补偿漂移信号非常困难，提出基于经验模态分解(EMD)的阈值滤波方法(EMD-T)对漂移信号预处理。为了提高EMD 分解的精确度，基于噪声传播模型引入一种有界噪声辅助分析的方法，将漂移信号中幅值小、频率高的噪声信息压缩至低阶本征模态函数中。为了验证算法的有效性，采集一款干涉型FOG 的静态漂移输出作为测试信号，将EMD-T 与基于小波包变换(WPT)和常规EMD(CEMD)的阈值滤波方法进行了对比分析。仿真结果及Allan方差分析表明，EMD-T较WPT和CEMD 滤波性能有显著的改善，经EMD-T 处理后，漂移信号的量化噪声(Q)和角度随机游走(N)分别由0.7862 μrad 和4.58×10-3（°）·h-1/2下降至0.1340 μrad 和9.03×10-4（°）·h-1/2。

The drift signal of fiber optic gyroscope (FOG) is often buried in noise. It is difficult to compensate drift directly, and a novel threshold filtering method based on empirical mode decomposition (EMD) (designated as EMD-T) is proposed as a pre-processing tool. Based on the noise spread model of EMD, a bounded noise assist analysis method is introduced to improve the decomposition accuracy of EMD. The noises with low magnitude and high frequency are compressed into early intrinsic mode functions. The static output of interferometric FOG is adopted to verify the effectiveness of EMD-T. Comparison analysis with filtering methods based on wavelet packet transform (WPT) and conventional EMD (CEMD) is done. Experimental and Allan variance analysis results show that EMD-T outperforms denoising method based on WPT and CEMD. The quantization noise (Q) and angle random walk (N) are decreased from 0.7862 μrad and 4.58×10-3（°）·h-1/2 to 0.1340 μrad and 9.03×10-4（°）·h-1/2, respectively, after applying EMD-T.