摘要
针对系统噪声不确定情况下的惯性导航系统非线性初始对准问题,提出了一种基于自适应组合滤波的初始对准方法.首先给出了一种基于Kalman/UKF组合滤波的神经网络实时训练算法;进而提出了基于Kalman/UKF组合滤波的非线性系统状态估计方法,该算法利用神经网络在线估计系统噪声,并利用Kalman/UKF组合滤波在线同时估计初始对准的状态量和神经网络的权值;最后将该算法应用于惯性导航系统非线性初始对准问题中,并进行了仿真研究.仿真结果表明:自适应组合滤波算法不仅保证了初始对准的精度,而且具有更好的实时性,是解决惯性导航非线性初始对准问题的一种有效且实用的方法.
In this paper,a neural network-aided adaptive Kalman/UKF integrated filter was studied for the nonlinear alignment of inertial navigation system.First a more robust learning algorithm for neural network based on the Kalman/UKF integrated filter is derived.Since it gives more accurate estimate of the linkweights, and the convergence performance is improved.This algorithm is then extended further to develop an adaptive Kalman/UKF integrated filter algorithm for state estimation of the nonlinear system.In this...
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2009年第11期1454-1459,共6页
Journal of Beijing University of Technology
基金
国家'八六三'计划资助项目(2006AA12Z305)
关键词
组合滤波
神经网络
惯性导航系统
初始对准
integrated filter
neural network
inertial navigation system
initial alignment
