面向空中目标作战意图分析的标准化全连接残差网络模型

Standardizedfullyconnectednetworkandresidual network model for combat intention analysis of air targets

针对复杂战场环境下传统空中目标作战意图识别方法存在缺乏自学习能力、依赖专家经验和面对大样本数据集的推理能力不足的问题,提出了一种面向空中目标作战意图分析的标准化全连接残差网络模型。空中战场态势属性因素作为输入,采用全连接网络将输入数据映射到高维空间,最终映射到样本标记空间,实现意图分析,利用批量归一化算法使网络着重学习非线性,加快网络收敛速度,添加残差网络使得在原有网络接近饱和时,继续提升网络的自学习能力。实验对模型训练和测试过程中准确率变化进行了分析,结果表明该方法可以快速准确识别目标的作战意图。

In order to solve the problems of lack of self-learning ability,dependence on expert experience and insufficient reasoning ability in the face of large sample data sets,a standardized fully connected network and residual network model for the analysis of combat intention of air targets in complex battlefield environment is proposed.The air battlefield situation attribute factor is used as the input,and the full connection network is used to map the input data to the high dimensional space,and finally to the sample tag space to realize the intention analysis.The batch normalization algorithm is used to make the network focus on learning nonlinear,speed up the convergence speed of the network,and add the residual network to continue to improve the self-learning ability of the network when the original network is close to saturation.The experiment analyzes the change of accuracy in the process of model training and testing,and the results show that the method can quickly and accurately identify the combat intention of the target.