WANG Lei, LI Shi-zhong
（College of Mechatronic Engineering, North University of China, Taiyuan 030051, China）
Abstract： In order to improve the accuracy of target intent recognition, a recognition method based on XGBoost（eXtreme Gradient Boosting）decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention. Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination. To verify the accuracy of recognition results, we compare the experimental results of this paper with the results in the literatures. The experiment shows that the probability of tactical intention recognition through this method is improved, so this method is feasible.
Key words： tactical intention recognition of target; XGBoost （eXtreme Gradient Boosting）decision tree; Dempster-Shafer combination rule
CLD number： E91 Document code： A
Article ID： 1674-8042（2018）02-0148-05 doi： 10.3969/j.issn.1674-8042.2018.02.008
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基于XGBoost决策树的空中目标战术意图识别
王磊， 李世中
（中北大学 机电工程学院， 山西 太原 030051）
摘要:为了提高空中目标战术意图识别的准确度， 提出了一种基于XGBoost决策树的目标意图识别方法。 以python为开发工具， 基于文献中的数据得到战术意图识别概率， 并依靠Dempster-Shafer证据合成理论得出贯序意图概率。 为了验证识别结果的准确性， 将其与文献中的实验结果进行对比。 结果表明， 该方法可提高目标真实意图的识别概率， 故该方法是可行的。
关键词:目标战术意图识别； XGBoost决策树； Dempster-Shafer证据合成理论
引用格式：WANG Lei, LI Shi-zhong. Tactical intention recognition of aerial target based on XGBoost decision tree. Journal of Measurement Science and Instrumentation, 2018, 9(2)： 148-152. ［doi： 10.3969／j.issn.1674-8042.2018.02.008］
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