Applying overlapping Allan variance theory to better stochastic modeling of microgyro

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 perform stochastic error modeling of microgyros.