基于灰色残差修正理论的目标航迹预测方法

Target Track Prediction Method Based on Grey Residual Modification Theory

针对灰色理论在智能反坦克子弹药对地面目标航迹预测的精度问题,提出两种灰色残差修正的航迹预测模型:标准灰色残差修正模型(GRMM)和基于灰色Verhulst模型理论的灰色Verhulst残差修正模型(GVRMM)。建立目标航迹灰色预测模型,并分析了灰色模型的局限性;对预测值与测量值的残差序列采用GRMM和GVRMM两种模型进行了在线预估,并利用残差预测值对航迹预测值进行实时修正。通过仿真实验验证了基于在线残差修正机制的方法能够有效减少目标跟踪误差,GVRMM的误差修正效果更加明显,具有良好的实用性。

Two grey residual modification models are proposed for the track prediction precision of brainpower antitank（ BAT） submunition based on grey theory,which are grey residual modification model（ GRMM） and grey Verhulst residual modification model（ GVRMM）. A grey model of target track prediction is established,and the limitations of this model are analyzed. GRMM and GVRMM are used to correct the grey forecast model on line,respectively. It shows that the proposed method based on realtime residual modification mechanism can be used to reduce the prediction error effectively,and GVRMM has better efficiency in improving track prediction precision.