多层神经网络在跟踪式卡尔曼滤波器中的应用  

A IMPROVED TRACKING KALMAN FILTER USING MULTILAYER NEURAL NETWORK

作　　者：韩明华[1] 袁乃昌[1] 

机构地区：[1]国防科技大学电子工程学院,长沙410073

出　　处：《电子科学学刊》1998年第6期739-744,共6页

基　　金：国家自然科学基金

摘　　要：本文将多层神经网络引入跟踪式卡尔曼滤波器中,提高了估计的精确度。以前的跟踪式卡尔曼滤波器的估计精度与目标的运动状态有关,当目标的运动不能够用线性状态空间模型描述时,其估计精度将要下降。而多层神经网络的引入,改善了这一不足。多层神经网络经过训练以后,能够对卡尔曼滤波器的结果进行修正。仿真结果表明,由于多层神经网络的应用,估计精度显著提高。This paper presents a method to improve the estimation accuracy of a tracking Kalman filter (TKF)by using a multilayer neural network(MNN). The estimation accuracy of the TKF is degraded due to the uncertainties that cannot be expressed by the linear state-space model proposed in the literature. This fault is overcome due to the use of MNN. The results of the TKF can be modified by the treated MNN. Simulation results show that the estimation accuracy is much improved by using the MNN.

关 键 词：目标跟踪 卡尔曼滤波 多层神经网络 滤波器 

分 类 号：TN713[电子电信—电路与系统] TP18[自动化与计算机技术—控制理论与控制工程]