面向格式化报文结构的数据链战术行为认知可行性初探

数据链所发送的报文往往承载着战术信息。在报文具体内容未知的情况下，对认知典型数据链战术行为的可行性进行了实验探究。首先根据格式化报文的特点，对包含战术消息的数据链V系列报文结构进行模拟，同时产生数据链的已调信号。然后利用小波包分解方法提取时频特征，构建时频图数据集，并使用深度学习方法对包含不同战术消息的信号进行分类，并引入应用于CNN的注意力机制模块CBAM，以进一步提高识别准确率。实验证明：即便报文结构未知，通过识别包含不同V系列战术消息的物理层信号，可以认知相应的战术任务。最后，基于实验结果，还提出一种新的数据链战术行为侦察流程新思路。

A New Data Link Tactical Behavior Reconnaissance Process

he messages sent by the data link often carry tactical information. In this paper, the specific content of the message being unknown, the feasibility of cognizing typical data link tactical behaviors is experimentally explored. Firstly, according to the characteristics of formatted messages, the structure of the V-series messages with a tactical message is simulated, and simultaneously, the modulated signal is of data link generated. And then the wavelet packet decomposition method is utilized for extracting the time-frequency features and constructing the time-frequency graph datasets. Deep learning is used to classify the signals containing different tactical messages, and the attention mechanism module CBAM applied to CNN is introduced to further improve the recognition accuracy.The experiments prove that even if the message structure is unknown, the corresponding tactical task can be recognized by identifying the physical layer signals containing different V-series tactical messages. Finally, based on the experimental results, a new data link tactical behavior reconnaissance process is proposed.