GP-RBF网络应用于飞行器外测数据野值实时剔除

测量数据集合中严重偏离大部分数据所呈现趋势的小部分数据点被称为野值点。野值的实时剔除对提高目标实时跟踪精度有十分重要的意义。采用了一种新的含有广义参数的径向基函数神经网络(GP-RBF网络)对航天靶场外测数据进行野值实时剔除。该网络可以通过对参数的自适应调整达到实时剔除野值的目的。采用经差商预处理后的飞行器弹道仿真数据，构成GP_RBF网络的训练集和测试集，试验结果表明利用该网络可以快速、有效地解决野值实时剔除问题。

Detection of Outliers from Spacecraft Tracking Data using GP-RBF Network

The data, accounting for a small proportion of the measured data, which deviate from the tendency showed by the measured data as a whole are called outliers. Outlier detection is significant to improving accuracy of target-tracking data. A new method based on a radial basis function neural network with general parameter (GP-RBF) is proposed for detecting outliers. The GP-RBF neural network can do on-line outlier detection by adjusting the general parameter adaptively. Simulation results show that this new method can resolve the problem of outlier detection efficiently and quickly.