基于自适应组合滤波的惯性导航系统初始对准方法

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.

Adaptive Integrated Filter for Initial Alignment of Inertial Navigation System

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 algorithm,the neural network is used to approximate the uncertainty of the system noise.And the Kalman/UKF integrated filter is used for both neural network online training and state estimation simultaneously.Simulation results show that the neural network-aided adaptive Kalman/UKF integrated filter algorithm is very effective for nonlinear initial alignment of inertial navigation system.Not only the initial alignment accuracy can surely be obtained,but also the time consumption of each run is reduced considerably.Consequently,an available and practicable algorithm for the nonlinear initial alignment of inertial navigation system is discovered.