对调制识别网络隐身的雷达发射信号生成方法

doi:10.12305/j.issn.1001-506X.2024.07.09

Abstract

Abstract:

The electronic reconnaissance system in radar countermeasure scenarios greatly improves the recognition accuracy of radar signals by introducing an intelligent pulse modulation recognition network based on deep learning methods. In order to improve the modulation stealth and anti-recognition ability of radar signals, a radar transmission signal generation method that can make the deep recognition network make incorrect predictions is proposed. Firstly, the time-frequency spectrum of the signal is obtained through short-time Fourier transform (STFT). Then, a time-frequency spectrum carrying modulated stealth information is generated iteratively. Finally, the improved inverse STFT is used to obtain the time-domain modulated stealth transmission signal. The radar signal generated by the proposed method is invisible to the modulation recognition network input from the time-frequency map, and can achieve pulse compression processing of the echo signal. The simulation results verified the effectiveness of the generated signal in resisting recognition, noise robustness, and pulse compression feasibility.

Key words: radar transmitting signal, time-frequency analysis, automatic modulation classification, radio frequency stealth, deep neural network (DNN)

CLC Number:

TN957

Ruibin ZHANG, Mengtao ZHU, Yunjie LI. Radar transmitting signal generation method for modulation recognition network stealth[J]. Systems Engineering and Electronics, 2024, 46(7): 2256-2268.

Figures/Tables 17

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调制类型	f(n)/Hz	φ(n)/rad
Barker	f₀	0, π
Costas	f_i∈{f₁, f₂, …, f_M}	φ₀
LFM	f₀+nK/2	φ₀

雷达信号	参数	区间
Barker	载频f_c/MHz	U[f_s/8, f_s/4]
	码长N_c	{7, 11, 13}
	循环周期数cpp	U[1, 2]
	码周期N_p	U[200, 400]
Costas	载频f_c/MHz	U[f_s/20, f_s/10]
	频率切换数N	U[3, 6]
	基频f_min/MHz	f_s/25
LFM	初始频率f₀/MHz	U[f_s/16, f_s/8]
LFM	带宽B/MHz	U[f_s/8, f_s/6]

调制识别模型	VGG11	VGG13	VGG16	VGG19	ResNet18	ResNet34	ResNet50	CNN-base
准确率	98.75	99.31	98.75	99.17	94.58	98.06	96.39	97.64

模型	攻击成功率	-10 dB	-6 dB	-2 dB	0 dB	2 dB	6 dB	10 dB	理想信号
VGG16	ASR1	96.67	97.78	98.89	95.56	94.44	88.89	92.21	92.22
	ASR2	96.55	95.55	95.51	95.35	96.47	95.00	95.45	98.82
	ASR3	80.95	77.15	83.81	85.71	82.86	84.76	85.71	92.38
	ASR	75.55	72.08	79.16	78.10	75.49	71.58	75.44	84.19
ResNet18	ASR1	98.89	91.11	93.33	88.89	86.67	82.22	77.78	77.78
	ASR2	94.38	94.25	94.32	94.12	93.75	93.56	95.77	96.15
	ASR3	100.00	100.00	98.10	100.00	100.00	100.00	100.00	99.05
	ASR	93.33	85.87	86.36	83.66	81.25	76.93	74.49	74.08
CNN-base	ASR1	100.00	100.00	98.89	98.89	100.00	98.89	95.56	92.22
	ASR2	97.78	100.00	98.89	100.00	100.00	98.88	100.00	100.00
	ASR3	79.05	77.14	85.72	95.24	90.47	87.62	93.33	92.38
	ASR	77.30	77.14	83.83	94.18	90.47	85.68	89.19	85.19

模型	信号类型	-10 dB	-6 dB	-2 dB	0 dB	2 dB	6 dB	10 dB	理想信号
VGG16	Barker	6.05	5.72	5.32	5.02	4.46	3.54	2.86	1.67
	Costas	5.55	5.51	5.17	4.96	4.07	3.58	2.70	1.40
	LFM	5.78	5.82	5.12	5.04	4.69	3.92	2.93	1.40
ResNet18	Barker	6.21	5.87	5.43	5.02	3.25	3.47	2.76	2.37
	Costas	5.94	5.49	5.49	4.69	4.52	4.51	3.76	2.69
	LFM	5.88	5.79	5.35	5.66	5.08	4.35	4.73	3.77
CNN-base	Barker	6.08	5.60	4.89	5.16	4.46	3.54	3.11	5.28
	Costas	5.69	5.48	5.35	4.75	4.65	4.05	3.67	5.14
	LFM	5.83	5.49	5.04	5.52	4.74	3.78	3.76	7.14

Radar transmitting signal generation method for modulation recognition network stealth

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隐身信号生成方法	替代识别模型	目标识别模型
隐身信号生成方法	替代识别模型	VGG11	VGG13	VGG16	VGG19	ResNet18	ResNet34	ResNet50	CNN-base
本文方法	VGG16	43.33	41.11	98.89(白)	53.33	40.00	37.78	36.67	23.33
	ResNet18	65.56	66.67	66.67	52.23	100.00(白)	72.22	78.89	22.22
	CNN-base	74.67	78.89	48.89	76.67	42.22	47.78	55.56	97.77(白)
文献[27]方法	VGG16	9.33	7.58	10.38	10.49	8.39	9.67	11.77	14.92
	ResNet18	1.74	1.62	1.53	1.85	1.04	1.64	2.03	2.33
	CNN-base	4.33	3.15	7.08	3.74	6.10	4.33	6.30	8.07

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