基于特征融合的Fast-CNN的复杂波形调制识别

doi:10.12305/j.issn.1001-506X.2025.07.10

Abstract

Abstract:

Aiming at the complex electromagnetic environment, the radar signals are complex in type and agile in form, which leads to the problem of lower signal recognition rate and higher arithmetic complexity, a complex waveform modulation recognition method based on fast-convolutional neural network (Fast-CNN) with feature fusion is proposed. Firstly, a larger teacher network (feature fusion network) and a smaller student network (Fast-CNN) are designed. The teacher network extracts and fuses the different scale features of the feature map to improve the network recognition rate. The student network removes the redundant channels by pruning method to solve the problem of larger computation. Then, the knowledge trained in the teacher network is transferred to the student network through knowledge distillation. Thus, the network can significantly reduce the amount of computation while maintaining the recognition accuracy. Experiments show that the overall recognition rate of the proposed method for 10 types of complex modulated waveforms reaches more than 99% when the signal-to-noise ratio is greater than -3 dB.

Key words: complex waveform, modulation recognition, feature fusion, network pruning, knowledge distillation

CLC Number:

TN974

Dongping ZHOU, Hang RUAN, Minghui SHA, Chongyu WANG, Nianqiang CUI, Yaobing LU. Complex waveform modulation recognition based on feature fusion with Fast-CNN[J]. Systems Engineering and Electronics, 2025, 47(7): 2165-2175.

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序号	网络结构	输出大小
第1部分	2×FF1+MaxPooling	64×27×27
第2部分	3×FF2+MaxPooling	128×13×13
第3部分	3×FF2+MaxPooling	128×6×6
第4部分	3×FF2+MaxPooling	256×3×3
第5部分	3×FF2+AvgPooling	256×1×1
第6部分	最大池化+全连接层	10(分类数量)

雷达波形	参数设置	取值范围
-	信号脉宽/μs	2
	采样率/MHz	500
	起始频率/GHz	1~3
LFM NLFM	信号带宽/MHz	30~200
BPSK/LFM	信号带宽/MHz	30~200
BPSK/LFM	巴克码长度	{7, 11, 13}
MPSK/LFM	信号带宽/MHz	30~200
MPSK/LFM	相位控制数	{4, 5, 6}
StepFrequency/LFM Costas/LFM	子脉冲带宽/MHz	5~30
	跳频间隔/MHz	10~30
	频率编码个数	{4, 5, 6, 7}
StepFrequency/BPSK Costas/BPSK	跳频间隔/MHz	10~30
	频率编码个数	{4, 5, 6, 7}
	巴克码长度	{7, 11, 13}
StepFrequency/MPSK Costas/MPSK	跳频间隔/MHz	10~30
	频率编码个数	{4, 5, 6, 7}
	相位控制数	{4, 5, 6}

SNR/dB	识别准确率	SNR/dB	识别准确率
-8	85.1	-1	99.8
-7	90.9	0	100.0
-6	95.2	1	100.0
-5	98.3	2	100.0
-4	98.9	3	99.8
-3	99.4	4	99.8
-2	99.9	-	-

剪枝率/%	剪枝后每个卷积层通道个数	参数量(×10³)	参数减少率/%	总体识别率/%
0(未剪枝)	[32, 32, 64, 64, 64, 64, 128, 128, 128, 128]	665.9	0	97.36
30	[25, 24, 46, 53, 48, 51, 116, 103, 78, 38]	348.9	47.60	97.47
50	[18, 20, 37, 32, 34, 39, 81, 69, 61, 24,]	176.5	73.49	97.24
70	[14, 16, 21, 19, 22, 24, 43, 44, 36, 10]	63.8	90.42	96.95
80	[6, 11, 16, 15, 13, 13, 26, 27, 31, 8]	27.9	95.81	96.36
82	[6, 8, 16, 14, 13, 12, 25, 24, 24, 7]	22.4	96.64	96.10
84	[6, 7, 16, 10, 13, 10, 18, 24, 22, 7]	17.7	97.34	95.28
86	[5, 7, 13, 10, 12, 9, 12, 20, 22, 6]	13.4	97.99	94.48
88	[5, 5, 11, 10, 10, 8, 8, 18, 21, 3]	9.8	98.53	53.05
90	[5, 4, 11, 9, 9, 6, 5, 10, 21, 3]	6.4	99.04	39.50

α	T	总体识别率/%
0.1	0.5	96.88
0.1	1	96.97
0.1	5	97.14
0.1	10	96.93
0.3	0.5	97.49
0.3	1	97.59
0.3	5	97.68
0.3	10	97.75
0.5	0.5	97.59
0.5	1	97.47
0.5	5	97.64
0.5	10	97.65
0.7	0.5	97.28
0.7	1	96.97
0.7	5	96.95
0.7	10	97.37

Complex waveform modulation recognition based on feature fusion with Fast-CNN

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网络	参数量(×10⁶)
AlexNet	26.10
GoogleNet	5.98
ResNet	11.17
SENet	21.45
FF-Fast-CNN	0.35

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