改进灰色残差模型在航迹预测中的应用

Application of Improved Grey Residual Model in Track Prediction

为了提高航迹预测的准确度,加强对来袭目标的打击效果,提出一种改进灰色残差模型对目标航迹进行预测。该方法将灰色GM(1,1)模型与支持向量回归机相结合,通过对原始航迹数据建立灰色模型得到预测值以及残差序列,提出的改进残差模型运用支持向量回归机非线性拟合的能力对预测值进行修正,在一定程度上克服了GM(1,1)模型的缺陷,使模型预测结果的精度得以提升。分别使用灰色GM(1,1)模型、灰色残差GM(1,1)模型、改进灰色残差GM(1,1)模型3种方法对同一航迹进行预测并对预测的结果进行对比。计算结果表明:该改进模型在航迹数据变化较大的情况下能够较为精确地对航迹进行预测,有较高的理论和实用参考价值。

In order to improve the accuracy of the prediction of the track, and strengthen the impact of the attack on the target, an improved grey residual model is proposed to predict the target track. The method of grey GM（1,1） model with support vector regression combination, obtained by predictive value and residual sequence of the original track data to establish the grey model, the improved residual model is used to correct the predictive value by using the nonlinear fitting ability of support vector regression, to a certain extent overcome the GM（1,1） model the model defects, the prediction accuracy can be improved. Using the grey GM（1,1） model, the grey residual GM（1,1） model and the improved grey residual GM（1,1） model, 3 methods are used to predict the same track and to compare the predicted results. The results show that the improved model can predict the track accurately with the large variation of track data, which has a high theoretical and practical reference value.