TY - JOUR
T1 - Applying overlapping Allan variance theory to better stochastic modeling of microgyro
AU - Li, Xiaoying
AU - Hu, Min
AU - Zhang, Peng
AU - Chang, Honglong
PY - 2007/4
Y1 - 2007/4
N2 - 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.
AB - 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.
KW - Drift
KW - Microgyro
KW - Overlapping Allan variance
KW - Stochastic error modeling
UR - http://www.scopus.com/inward/record.url?scp=34250376487&partnerID=8YFLogxK
M3 - 文章
AN - SCOPUS:34250376487
SN - 1000-2758
VL - 25
SP - 225
EP - 229
JO - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
IS - 2
ER -