集中式GNSS/INS深组合模型及算法研究

研究了集中式深组合量测模型;并在此基础上研究了深组合滤波算法。由于集中式深组合系统具有强非线性、状态向量维数高、计算量大等特点,将一种降维容积卡尔曼滤波算法(reduced-dimension cubature kalman filter,RCKF)应用于集中式深组合滤波中。相较于常规容积卡尔曼滤波算法(cubature kalman filter,CKF),该算法仅对状态向量中与量测矩阵非线性有关向量采样,减少采样向量维数从而降低滤波计算量。通过仿真实验证明该算法在不损失精度的情况下,可大大减少组合滤波计算量。

Study on model and algorithm of the centralized ultra-tight GNSS/INS integration

This paper studies the centralized ultra-tightly integrated measurement model, and studies the ultra-tight integration filtering algorithm on the basis of the measurement model. Due to the characteristics of centralized ultra-tight integration with strong nonlinear, high dimension of state vector, the large amount of calculation, this paper applies a reduced-dimension cubature kalman filter (RCKF) algorithm to the centralized ultra-tight integration. Comparing with conventional CKF, only the elements in the state vector related to the nonlinear measurement information are sampled. As a result, the RCKF algorithm reduces the dimension of the sampling vectors, which reduces the calculation amount. The simulation shows that the RCKF algorithm can greatly reduce the amount of calculation in integrated filter without loss of accuracy.