基于SVM和EKF的高超声速滑翔飞行器轨迹预报

高超声速滑翔飞行器（HGV）拦截问题中，轨迹预报是成功拦截的重要基础。针对HGV机动能力强、轨迹多变的特点，提出了一种基于支持向量机（SVM）和扩展卡尔曼滤波（EKF）的轨迹预报方法。在HGV的滑翔段机动模式分析的基础上，将HGV的机动运动分解为纵向运动模式和侧向运动模式，进而对运动模式的特征参数予以标定，形成SVM的训练集。建立地基单雷达轨迹跟踪模型，采用EKF对HGV滑翔段轨迹进行稳定跟踪并实现对运动模式特征参数的估计。基于SVM，建立了HGV运动识别框架，实现了对HGV滑翔段轨迹的预报。对平衡滑翔和跳跃机动2种典型机动模式进行数学仿真验证，结果表明，所提方法可以提高对该类目标的轨迹预报精度。 

Trajectory prediction of hypersonic glide vehicle based on SVM and EKF

In the scenario of intercepting a Hypersonic Glide Vehicle (HGV), the trajectory prediction is a key issue for successful interception. Considering HGV's strong maneuverability and variable trajectory, in this paper, a novel trajectory prediction method is proposed based on Support Vector Machine (SVM) and Extended Kalman Filter (EKF). First, the investigation on the maneuvering mode is performed. The maneuver motion of the HGV is divided into longitudinal mode and lateral mode, which are labeled and formulated into the training set of SVMs. Second, the tracking model of the trajectory for single ground-based radar is established, and EKF is applied to track the glide trajectory of HGV. Finally, the recognition framework of HGV motion is established based on SVM, and the prediction of the subsequent trajectory is accomplished. The results show that the proposed method can improve the trajectory prediction accuracy of HGV.