基于组合滤波的光纤陀螺惯导/星敏感器组合导航算法

Algorithm based on combined filters for integrated navigation systems of FOG INS and star sensor

  • 摘要: 针对卡尔曼滤波在连续非线性的惯性组合导航系统中对模型误差估计不够准确的问题,提出了利用可直接处理连续非线性系统的预测滤波为卡尔曼滤波提供一步预测的组合滤波算法,弥补了两种滤波算法单独使用时的不足,从而提高了导航系统精度。再利用光纤陀螺惯导实测数据与计算机生成的星敏感器数据对文中组合滤波算法进行了离线仿真,证明了文中组合滤波算法的可行性、优越性。

     

    Abstract: In order to solve the problem that Kalman filter(KF) used in continuous, non-linear inertial navigation system(INS) suffered low accuracy in estimating model error, model predictive filter(MPF) directly processing continuous, non-linear system was adopted to provide one-step prediction for KF. Combining the advantages of the two filter algorithm, the accuracy of navigation could be further improved. Based on data tested by fiber-optic gyro(FOG) INS and star sensor(SS) data provided by computer, a simulation was executed for the presented combined filters, which proved the feasibility and superiority of the combined filtering algorithm in this paper.

     

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