基于LDA-TabTransformer的海上目标威胁评估  

Maritime Target Threat Assessment Based on LDA-TabTransformer

作　　者：吴正威 WU Zhengwei(Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学,湖北武汉430068

出　　处：《现代信息科技》2025年第4期53-57,63,共6页Modern Information Technology

摘　　要：在现代海战场中,复杂多变的环境使得目标威胁评估的准确性面临诸多挑战。针对海上目标的有效评估和复杂评估因素的分析,提出了一种基于深度学习的海上目标威胁评估方法。根据复杂海战场的环境因素和目标属性两个角度,构建了海上目标威胁评估指标体系,将数据类型分为类别特征与数值特征,对海上目标威胁评估进行分析。引入TabTransformer模型为基础,加入线性判别分析对该模型进行优化,建立了目标威胁评估模型,并通过仿真数据进行训练测试,模型的准确率约为91%,鲁棒性强并为海上目标威胁评估提供了新的解决方案,具有广泛的应用前景。In modern maritime battlefields,the complex and changeable environment poses numerous challenges to the accuracy of target threat assessment.To address the effective evaluation of maritime targets and the analysis of complex assessment factors,a method for maritime target threat assessment based on Deep Learning is proposed.According to the two perspectives of environmental factors and target attributes in complex maritime battlefields,an index system for maritime target threat assessment is constructed.The data types are categorized into categorical and numerical features for the analysis of maritime target threat assessment.Based on the introduced TabTransformer model,Linear Discriminant Analysis is incorporated to optimize the model,and a target threat assessment model is established.Through training and testing with simulated data,the accuracy of the model is approximately 91%.The model has strong robustness,provides a new solution for maritime target threat assessment,and has broad application prospects.

关 键 词：威胁评估 线性判别分析 深度学习 多头自注意力机制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP39[自动化与计算机技术—控制科学与工程]