集中式GNSS/INS深组合模型及算法研究  

Study on Model and Algorithm of the Centralized Ultra-tight GNSS / INS Integration

作　　者：王鹏[1] 蔡爱华[2] 

机构地区：[1]电子科技大学电子科学技术研究院,成都611731 [2]中国电子科学研究院,北京100041

出　　处：《科学技术与工程》2015年第13期205-211,217,共8页Science Technology and Engineering

摘　　要：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。The centralized ultra-tightly integrated measurement model was studied, and studied 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, a reduced- dimension cubature Kalman filter （RCKF） algorithm was applied to the centralized ultra-tight integration. Compa- ring 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.

关 键 词：集中式深组合 深组合量测模型 降维CKF 

分 类 号：V249.328[航空宇航科学与技术—飞行器设计]