中文摘要：

微机械陀螺随机误差的辨识对提高微机械陀螺的精度具有非常重要的意义。在相同的置信水平下，交叠式Allan方差分析方法比普通Allan方差具有更大的置信区间。论文对交叠式Allan方差在微机械陀螺随机误差辨识中的应用做了深入研究，并基于该方法对某型号微机械陀螺进行了随机误差辨识实验。实验表明该方法能有效地辨识出微机械陀螺的各项随机误差成分。

英文摘要：

Aim. Li et al applied Allan variance theory to stochastic modeling of microgyro^[3]. We believe that applying overlapping Allan variance theory to such modeling is better because the stochastic errors of microgyro have two characteristics： instability and slow drift. In the full paper, we explain our better modeling method in detail; in this abstract, we just add some pertinent remarks to listing the two topics of explanation： （1） overlapping Allan variance theory, （2） stochastic error identification platform; in topic 1, eqs. （1） through （6） and Table 1 in the full paper are taken from the open literature; also in topic 1, we determine sample length, which is needed in applying overlapping Allan variance theory, with eq. （8）, which is actually the same as eq. （7） taken from the open literature but put into a different form for convenience in application; in topic 2, we give Fig. 4 in the full paper showing the flow chart of the modeling platform constructed by us. Finally we conducted a stochastic error identification experiment using three microgyros to verify the overlapping Allan variance method. The experimental results, shown in Fig. 6 and Table 4 in the full paper, indicate that, in general, the error coefficients are in good agreement with the given performance parameters. Our overlapping Allan variance method can effectively and accurately oerform stochastic error modeling of microgyros.