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研究了一种模糊自适应卡尔曼滤波器(FAKF),并将其应用于惯性/多普勒组合导航中,以解决量测噪声时变导致滤波精度下降的问题。模糊自适应卡尔曼滤波器将模糊控制理论应用于卡尔曼滤波,通过新息来调节量测噪声的方差矩阵,使其与实际的量测方差相匹配,达到最优估计的目的。机载试验数据的离线仿真表明,FAKF能够较好地适应量测噪声时变的情况,对惯性/多普勒组合导航,采用FAKF的速度误差估计精度比采用标准卡尔曼滤波提高(0.05~0.1)m/s,达到了0.18m/s(1σ)。
A fuzzy adaptive Kalman filter (FAKF) is studied and applied to inertial / Doppler integrated navigation in order to solve the problem that the time-varying of measurement noise leads to the decrease of filtering accuracy. Fuzzy Adaptive Kalman Filter The fuzzy control theory is applied to Kalman filter, and the variance of measurement noise is adjusted by new interest to match with the actual measurement variance to achieve the optimal estimation. Off-line simulation results of airborne experimental data show that FAKF can better adapt to time-varying measurement noise. For inertial / Doppler integrated navigation, the accuracy of FAKF is higher than that of standard Kalman filter (0.05 ~ 0.1 ) m / s, reaching 0.18 m / s (1σ).