摘要
弹道外推技术在炮位雷达的侦察和校射中起关键作用，弹道外推的精度直接决定着炮位侦察校射雷达的性能。在文献[1]中，作者提出了将弹道外推分为弹道识别和特定弹道外推两个阶段，并用支持向量机方法对弹道识别进行了系统研究。文中引进Boosting学习算法进行弹道识别。仿真结果表明，基于决策树的Boosting学习算法是一种有效的弹道识别方法，并且识别精度高于基于核技巧的支持向量机方法。
Trajectory prediction plays a crucial role in reconnaissance and adjustment of radar, and the performance of radar for re- connaissance and adjustment is directly determined by its accuracy. In our paper [1], it was proposed that the stage of trajectory prediction can be divided into the recognition phase and prediction phase of specific trajectories, and the application of SVM in trajectory recognition was systematically investigated. In this paper, the Boosting classification technique was introduced to recognize the trajectories. Several experiments indicate that the efficient decision-tree-based Boosting algorithms reach higher precision than kernel-based SVM.
出处
《弹箭与制导学报》
CSCD
北大核心
2010年第4期193-196,共4页
Journal of Projectiles,Rockets,Missiles and Guidance
基金
国家自然科学基金(60835002,60975040)资助
关键词
弹道外推
弹道识别
机器学习
支持向量机
BOOSTING
traj ectory prediction
trajectory recognition
machine learning
support vector machine
Boosting
