摘要
以地-地导弹的惯导系统为研究对象,分析了传统方法在惯导系统初始对准方面的缺陷。针对惯导系统的非线性及实时性等方面的要求,考虑到神经网络所具有的函数逼近性能,扩展Kalman滤波(EKF)所具有的最优估计性能的特点,提出了基于扩展Kalman滤波的神经网络应用技术。应用扩展Kalman滤波对多层神经网络的非线性离散时间系统进行算法训练,在获得的所有观测数据中找到状态(权值)的最小方差估计。在假定的地理坐标系下,对地-地导弹的惯导系统地面自对准的非线性状态方程,应用Matlab对基于EKF的神经网络方法和传统的Kalman滤波方法进行了仿真,对仿真的结果进行了对比分析。
We studied the Inertial Navigation System（INS） of ground-to-ground missile, and analyzed the drawbacks of traditional methods in initial alignment of INS. INS has the requests in non-linear and real-time performance. Considering the function-approximation performance of neural network, and the optimization-esti- mation performance of Extending Kalman Filtering （EKF）, we put forward the technique of neural network based on EKF. EKF was used for arithmetic training of non-linear discrete time system of multi-level neural network, the minimum variance estimation of weighted values was found in all the observed data. To study the nonlinear state equation of auto-alignment system of INS in ground-to-ground missile, we carried out simula- tions by use of Matlab for EKF based neural-network method and traditional Kalman filtering method under supposed geography coordinates. The result of simulations was analyzed in this paper.
出处
《电光与控制》
北大核心
2008年第1期17-21,共5页
Electronics Optics & Control
关键词
地地导弹
惯导系统
扩展Kalman滤波
神经网络
初始对准
非线性
ground-to-ground missile
inertial navigation system
extending Kalman filtering
neuralnetwork
initial alignment
non-linear
