摘要
在利用卡尔曼滤波器对数据进行处理时,其计算时间是由模型的状态矢量维数n决定的,每一步迭代的计算量与n3成正比。状态维数的减少会使计算时间大大缩短。本文首先介绍了SINS/CNS组合导航系统的动态模型,研究了基于奇异值分解的状态可观测度分析方法并提出一种改进的方法,在求状态变量的可观测度时,抛开了观测量,只利用可观测矩阵进行分析,然后应用该理论对SINS/CNS组合导航系统进行状态可观测度的分析,略去不可观测的状态分量,提出一种降阶的系统模型。仿真结果证明,降阶模型可以提供满意的导航精度。
The main factor in determining the computation time of Kalman filter is the dimension of the model state vector. The number of computations per iteration is on the order of n^3. Any reduction in the number of states will significantly decrease the computation time. SINS/CNS integrated navigation system model is introduced in this paper. The observable degree analysis of the system states based on the singular value decomposition is researched and a new method is proposed. The method is applied in the SINS/CNS integrated navigation system and a reduced - dimension model is proposed. The simulation results prove that reduced-dimension model can also provide satisfactory accuracy for aircraft navigation.
出处
《航天控制》
CSCD
北大核心
2005年第6期12-16,共5页
Aerospace Control
基金
总装十五预研项目"卫星复合导航关键技术研究"(413220403)
关键词
组合导航
卡尔曼滤波
可观测度
降阶模型
Integrated navigation system Kalman filtering Observable degree Reduced-dimention model
