基于PACA的复杂空中目标战术意图识别方法

Tactical Intent Recognition of Complex Air Targets Based on PACA

针对战场中空中目标航迹动态性、时序变化性及意图多样的特性,提出一种基于端到端类属属性学习的识别方法,作为智能多意图识别模型的基本框架。融合目标航迹中的时序特征及属性特点,通过压缩及修正预处理统一输入编码信息,封装专家的知识经验为标签,学习指挥员战时情况判断的思维方式,消除其隐蔽性、欺骗性和对抗性所带来的干扰因素,得出特定目标的复杂战术意图。通过仿真实验,采用常用多分类评价体系分析端到端训练方式对结果的影响,以及与相关方法的对比分析表明,所提算法针对多意图识别更具有效性和参考价值,可用于支撑作战筹划系统建立非合作目标与保卫要地的关联关系。

In view of the dynamic nature of air target track,time sequence variation and multiple intentions in battlefield,this paper proposes a recognition method based on end-to-end generic attribute learning,which is used as the basic framework of intelligent multi-intention recognition model.By integrating the time sequence features and attribute characteristics of the target track,the unified input coding information is compressed and modified to encapsulate the knowledge and experience of experts as labels,and the commander's thinking mode of wartime situation judgment is learned to eliminate the interference factors brought by its concealment,deception and confrontation,so as to obtain the complex tactical intention of the specific target.Through the simulation experiment,the common multi-classification evaluation system is used to analyze the influence of the end-to-end training method on the results,and the comparison analysis with the relevant methods shows that the proposed algorithm is more effective and valuable for multi-intention recognition,and can be used to support the establishment of the correlation between non-cooperative targets and defense important places in the combat planning system.