基于混合信号多域特征和Transformer的干扰识别

doi:10.12305/j.issn.1001-506X.2024.06.32

Abstract

Abstract:

Aiming at the vulnerability of wireless communication channels to interference from intentional radio frequercy signals, a method of interference identification from mixed signals is presented. In this work, a novel Transformer model named Multidomain-former is devised to serve as the multidomain features extractor and jamming types recognizer. The proposed model is built with the following characteristics: the original spectral data is firstly preprocessed by a specific sequence partitioning mechanism. Meanwhile, the initial sequence features are reserved by linear embedding and position coding. Secondly, an encoding module which jointly adopting inverse Fourier transform and Fourier transform is designed, by this means Multidomain-former can obtain both frequency domain and time domain features. A real wireless transceiver channel is established utilizing universal instruments and transceiver antennas, and the mixed signal spectrum is collected subject to different jamming-to-signal ratios (JSR) to form training and test data sets. The interference classification experiments are carried out sequentially by the proposed Multidomain-former, in comparison with the classic Transformer and other popular deep learning networks as well. It is shown that Multidomain-former achieves the best performance with the least number of parameters and lower complexity. With the condition of the JSR is less than 10 dB, the probability for correct classification of Multidomain-former is 2%~3% higher than that of classic Transformer. When the JSR is equal to -5 dB, the performance of Multidomain-former is proofed to increase by 3.0%~9.3% on correct classification rate compared with other benchmarks.

Key words: mixed signal, multidomain feature extraction, interference identification, Transformer, jamming-to-signal ratio (JSR)

CLC Number:

TN973.1

Pengfei YANG, Ling HE, Qian WANG, Ruidi WANG, Mingzhi ZHANG. Interference identification based on mixed signal multidomain feature and Transformer framework[J]. Systems Engineering and Electronics, 2024, 46(6): 2138-2145.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Table 3

Fig.4

Table 4

Fig.5

Fig.6

References 17

1	周红平, 马明辉, 吴若无, 等. 基于稀疏表示分类的雷达欺骗干扰识别方法[J]. 系统工程与电子技术, 2022, 44 (9): 2791- 2799.
	ZHOU H P , MA M H , WU R W , et al. Deception jamming recog-nition of radar based on sparse representation classification[J]. Systems Engineering and Electronics, 2022, 44 (9): 2791- 2799.
2	MUGHAL M O , KIM S . Signal classification and jamming detection in wide-band radios using Naive Bayes classifier[J]. IEEE Communications Letters, 2018, 22 (7): 1398- 1401. doi: 10.1109/LCOMM.2018.2830769
3	SHI Y X , LU X J , NIU Y T , et al. Efficient jamming identification in wireless communication: using small sample data driven Naive Bayes classifier[J]. IEEE Wireless Communications Letters, 2021, 10 (7): 1375- 1379. doi: 10.1109/LWC.2021.3064843
4	ZHANG L , REN J , LI T T . Time-varying jamming modeling and classification[J]. IEEE Trans.on Signal Processing, 2012, 60 (7): 3902- 3907. doi: 10.1109/TSP.2012.2193574
5	SHAO G Q , CHEN Y S , WEI Y S . Convolutional neural network-based radar jamming signal classification with sufficient and limited samples[J]. IEEE Access, 2020, 8, 80588- 80598. doi: 10.1109/ACCESS.2020.2990629
6	HAN H , LI W , FENA Z B , et al. Proceed from known to unknown: jamming pattern recognition under open-set setting[J]. IEEE Wireless Communications Letters, 2022, 11 (4): 693- 697. doi: 10.1109/LWC.2021.3140145
7	靳增源, 张晓瀛, 谭思源, 等. 基于集成时频通道注意力的倒残差神经网络干扰识别[J]. 信号处理, 2023, 39 (2): 343- 355.
	JIN Z Y , ZHANG X Y , TAN S Y , et al. Jamming identification based on inverse residual neural network with integrated time-frequency channel attention[J]. Journal of Signal Processing, 2023, 39 (2): 343- 355.
8	SHAO G G , CHEN Y S , WEI Y S . Deep fusion for radar jamming signal classification based on CNN[J]. IEEE Access, 2020, 8, 117236- 117244. doi: 10.1109/ACCESS.2020.3004188
9	邵正途, 许登荣, 徐文利, 等. 基于LSTM和残差网络的雷达有源干扰识别[J]. 系统工程与电子技术, 2023, 45 (2): 416- 423.
	SHAO Z T , XU D R , XU W L , et al. Radar active jamming recognition based on LSTM and residual network[J]. Systems Engineering and Electronics, 2023, 45 (2): 416- 423.
10	王李静. 航天测控链路干扰多维感知及决策研究[D]. 成都: 电子科技大学, 2020.
	WANG L J. Research on multidimensional perception and decision-marking of interference in space TT&C links[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
11	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proc. of the Advances in Neural Information Processing Systems, 2017.
12	DOSOVITSHIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[EB/OL]. [2023-05-10]. https://arxiv.org/abs/2010.11929.
13	IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size[EB/OL]. [2023-05-10]. https://arxiv.org/abs/1602.07360.
14	MA N, ZHANG X, ZHENG H T, et al. Shufflenet v2: practical guidelines for efficient CNN architecture design[C]//Proc. of the European conference on computer vision, 2018: 116-131.
15	HOWARD A, SANDLER M, CHU G, et al. Searching for MobileNetV3[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2019: 1314-1324.
16	TAN M X, LE Q. EfficientNetV2: Smaller models and faster training[C]//Proc. of the International Conference on Machine Learning, 2021: 10096-10106.
17	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780. doi: 10.1162/neco.1997.9.8.1735

仪器设备功能	型号	仪器设备设置
SOI模拟产生	Agilent E4438C	CF=3 GHz, BW=20 MHz, 发射功率固定为P_s(单位: dB)
干扰信号模拟产生	Ceyear 1465D	CF=3 GHz, BW及信号按照典型干扰类型定义产生, 发射功率P_j(单位: dB)可调, 调节范围为JSR(dB)=P_j-P_s, JSR∈{-5, 0, 5, 10, 15, 20}
混合信号频谱分析	Keysight N9030B	CF=3 GHz, Span=50 MHz, RBW=20 kHz, VBW=20 kHz, 平均类型: Log-Power, FFT点数为4 096

信号	信号类型/体制	参数设置
SOI s(t)	直序扩频-跳频-BPSK	载频3 GHz, 跳频带宽100 MHz, gold码扩频码速10 Mcps, 跳频点数2 048, 跳频间隔40 kHz
干扰信号J(t)	MTJ	中心频率3 GHz, 音频数5, 频率间隔2 MHz
	PJ	中心频率3 GHz, 脉冲宽度200 ns, 脉冲周期1 μs
	LFMJ	扫描范围2.99~3.01 GHz, 单次扫描时间10 ms
	PBNJ	中心频率3 GHz, 带宽6 MHz
	PBWJ	中心频率3 GHz, 带宽12 MHz

子帧长度L	参数量(×10⁶)	FLOPS(×10⁶)	平均正确识别率/%
32	0.11	1.16	81.4
48	0.21	2.48	82.0
64	0.34	4.29	83.0
80	0.49	6.59	83.0
96	0.69	9.38	82.4

网络模型名称	参数量(×10⁶)	FLOPS(×10⁶)
SqueezeNet	0.73	6.43
ShuffleNetV2	1.26	11.77
MobileNetV3	1.52	5.48
EfficientNetV2	20.18	237.51
LSTM	1.23	2.29
Multidomain-former	0.34	4.29

[1]	Han ZOU, Tianqi ZHANG, Kunran MA, Zongfang YANG. Single-mixed signal modulation and recognition algorithm for MIMO-OFDM system based on multi-feature fusion [J]. Systems Engineering and Electronics, 2024, 46(4): 1456-1465.
[2]	Zhen YAO, Chuang YANG, Xiaodong JI. Fast estimation algorithm for DOA of mixed signal in low snapshots [J]. Systems Engineering and Electronics, 2024, 46(2): 722-728.
[3]	Lan MA, Shijun MENG, Zhijun WU. Intention mining for civil aviation radiotelephony communication based on BERT and generative adversarial [J]. Systems Engineering and Electronics, 2024, 46(2): 740-750.
[4]	Zichang LIU, Yongsheng BAI, Siyu LI, Xisheng JIA. Diesel engine fault diagnosis method based on wavelet time-frequency diagram and Swin Transformer [J]. Systems Engineering and Electronics, 2023, 45(9): 2986-2998.
[5]	Haigang SUI, Jiajie LI, Guohua GOU. Online fast localization method of UAVs based on heterologous image matching [J]. Systems Engineering and Electronics, 2023, 45(10): 3008-3015.
[6]	Kun QIAN, Chenxuan LI, Meishan CHEN, Jiwei GUO, Lei PAN. Ship target instance segmentation algorithm based on improved Swin Transformer [J]. Systems Engineering and Electronics, 2023, 45(10): 3049-3057.
[7]	QI Dong, TANG Min, LIU Chengcheng, ZHAO Yongjun. Two-dimensional DOA estimation method for mixed signals under Gaussian color noise [J]. Systems Engineering and Electronics, 2019, 41(10): 2198-2204.
[8]	JING Xiao-rong, YANG Yang, ZHANG Zu-fan, CHEN Qian-bin. DOA estimation for mixed signals and mutual coupling self-calibration #br# for uniform linear array [J]. Systems Engineering and Electronics, 2014, 36(9): 1696-1701.

Interference identification based on mixed signal multidomain feature and Transformer framework

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 17

Related Articles 8

Recommended Articles

Metrics

Comments