摘要
对于实际战场中目标属性要素呈现出的多样化,传统目标意图识别方法不能够较全面地建立属性之间的相似度模型。为了更好地阐述实际战场的复杂情况,提高目标意图识别的准确度,提出了一种利用改进的空间相似度与属性相似度融合的高维数据相似度模型,以全面地计算目标各种属性状态对态势意图的支持程度,再利用得到的高维数据相似度通过D-S证据理论对目标进行序贯识别。仿真实验表明:该方法具有有效性以及能够提高目标意图识别的准确度,为解决目标战术意图识别提供了新的方法。
The methods of traditional target intention recognition can＇ t comprehensively establish model of similarity among attributes.In order to elaborate on the complex situation of actual battlefields,a high-dimensional data similarity model using the fusion of the modified spatial similarity and attribute similarity is proposed to roundly compute the support degree of various properties of target on situational intention,which contributes to improving the accuracy of target intention recognition,use the obtained high-dimensional data similarity to sequentially recognize targets by means of theory of D-S evidence.Simulation results show that the method is not only effective,but also improves the accuracy of target intention recognition,which open up a new way for solving the target tactical intention recognition.
出处
《传感器与微系统》
CSCD
2017年第5期25-28,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61170120)
关键词
意图识别
高维数据相似度
证据理论
intention recognition
high-dimensional data similarity
evidence theory
