改进DenseNet在电台建链行为识别时的可视化研究

doi:10.12305/j.issn.1001-506X.2021.05.26

Abstract

Abstract:

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.

Key words: visualization, behavior recognition, heat map, DenseNet, automatic link establishment behavior, short-wave radio station

CLC Number:

TP181

Zilong WU, Hong CHEN, Yingke LEI. Visualization research on improved DenseNet applied to recognize a radio station's link establishment behavior[J]. Systems Engineering and Electronics, 2021, 43(5): 1371-1381.

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References 21

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建链行为种类	功能
呼叫行为	一方电台想要和另一方电台建立通信(另一方电台可以是一个、一组或其余所有电台)
握手行为	当接收到另一方电台呼叫时, 电台想要继续通信而进行的行为
通知行为	一方电台以通知的形式向所有其他电台发出信息
时间校准行为	电台进行时间校准的通联行为
组时间广播行为	一方电台向处于同一个通信网络组的其余电台进行时间广播
广播行为	一方电台指示另一方电台执行某一命令
扫描呼叫行为	已建立过通信链路的双方电台再次进行通信所进行的呼叫行为

层	输入矩阵维度大小	输出矩阵维度大小
输入层	(5 888, 32)	(5 888, 32)
BN+ReLu+Conv	(5 888, 32)	(5 888, 32)
特征复用层	(5 888, 32), (5 888, 32)	(5 888, 64)
BN+ReLu+Conv	(5 888, 64)	(5 888, 32)
特征复用层	[(5 888, 32)]×3	(5 888, 96)
BN+ReLu+Conv	(5 888, 96)	(5 888, 32)
特征复用层	[(5 888, 32)]×4	(5 888, 128)
BN+ReLu+Conv	(5 888, 128)	(5 888, 32)
输出层	[(5 888, 32)]×5	(5 888, 160)

训练样本数目	识别准确率/%
1 400	56.34
2 800	63.11
4 200	65.88
5 600	68.32
7 000	69.83
8 400	70.79
9 800	71.23
11 200	72.94
12 600	73.38

训练样本数目	识别准确率/%
1 400	86.46
2 800	92.30
4 200	94.16
5 600	96.44
7 000	97.48
8 400	98.19
9 800	98.26
11 200	98.38
12 600	99.14

训练样本数目	识别准确率/%
1 400	99.58
2 800	99.83
4 200	99.85
5 600	99.89
7 000	99.90
8 400	99.93
9 800	99.98
11 200	99.98
12 600	99.99

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

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