摘要
为了克服传统卡尔曼滤波对观测噪声必须为零均值白噪声过程的依赖,针对工作于恶劣环境下的飞行器、舰船等运动载体的组合导航系统,提出了利用神经网络的自学习、自组织、自适应能力来辅助传统卡尔曼滤波器的方法。对所建立的BP神经网络和基于正交最小二乘(OLS)算法的RBF神经网络的收敛速度进行了比较。最终选择收敛较快的RBF神经网络辅助卡尔曼滤波器,仿真结果表明该方法能够抑制滤波器发散,提高了导航定位精度。
In order to overcome the dependency that the measured noise must be white noises with zero mean value in the routine Kalman filtering equations, The real environment of a moving vehicle′s integrated navigation system (aircraft or ship) is worse, a method of using the neural network′s capability of self-taught, self-organized and self-adapted to aid Kalman filter is proposed. The convergence speeds of back-propagation (BP) neural network (NN) and radial basis function(RBF) NN based on orthogonal least square...
出处
《红外与激光工程》
EI
CSCD
北大核心
2008年第S1期270-273,共4页
Infrared and Laser Engineering
基金
教育部新世纪人才支持计划(NCET-06-0462)
