改进DenseNet在电台建链行为识别时的可视化研究

Visualization research on improved DenseNet applied to recognize a radio station’s link establishment behavior

在未知通信协议标准条件下,改进DenseNet可以识别短波通信电台不同的自动建链行为,这对侦察非协作方电台的通联意图有着重要作用。但是网络模型识别的可靠性需要做进一步的探究。因此,本文通过网络内部结构的可视化来推断网络模型是否可以有效地提取建链行为信号的深层次特征。由于卷积层的特征维度较大,不利于观察建链行为信号的深层次特征,所以对网络内部全连接层进行了可视化分析。实验结果表明,网络模型可以有效提取同一类别行为信号深层次的相似特征以及不同类别行为信号深层次的差异特征,并通过热力图进一步提高了实验结果的可信度。

Under the condition that the communication protocol standard is unknown,the improved DenseNet can recognize a short-wave radio station’s different automatic link establishment behaviors,which plays an important role in detecting non-cooperative stations’communication intentions.However,the reliability of network model’s recognition needs to be further explored.Therefore,by visualizing the network’s internal structure,it is deduced whether the network model can effectively extract the deep features of the link establishment behaviors signals.The feature dimension of the convolutional layer is large,it is not conducive to observe the deep features of the link establishment behavior signal.So the visualization analysis of the full connected layer inside the network is carried out.The experimental results show that the network model can effectively extract the deep features’similarities of the same behavior signals and the deep features’differences of different behavior signals.And experiment of heat maps further improves the credibility of our experimental results.