基于蚁群优化的实时航迹灰色预测

A Grey Prediction for Real-Time Track Based on Ant Colony Optimization

对于空中目标实时航迹预测问题,在数据匮乏的情况下,灰色GM(1,1)预测模型是一种行之有效的方法,但实际工程应用中,灰色模型预测的精度时常达不到要求。通过对灰色模型内在原理逻辑的深入研究,指出了其初值和灰参数的选取并非最优。采用蚁群仿生算法对初值和灰参数进行优化,有效地提高了航迹预测的精度,通过实例计算验证了该方法的有效性和精确性。

As to the issue of predicting real-time aerial target track, grey prediction model is an effective method in the context of limited data. But the accuracy of grey model prediction often fails to meet the requirements in practical engineering applications. Through in-depth study on the modeling mechanism of grey mode, it was found that the selections of initial value and grey parameters are not optimal. Then, by using ant colony optimization for the initial value and grey parameters, the accuracy of track prediction was improved significantly. The effectiveness and precision of the method was verified through case calculation.