复杂多径环境下的无人机集群通信波形识别

doi:10.12305/j.issn.1001-506X.2023.10.36

Abstract

Abstract:

The electromagnetic environment of unmanned aerial vehicle (UAV) swarm communication is complex, and phenomena such as user interference, multipath fading and frequency shift frequently occur. The performance of the waveform recognition algorithm under the traditional additive white Gaussion noise channel is greatly reduced in this scenario. To solve this problem above, a waveform recognition algorithm for UAV swarm communication in complex multipath environment is proposed. Firstly, a multipath fading channel model for UAV swarm communication under Alpha pulse interference is established. Secondly, aiming at the problem of Alpha pulse interference among swarm users, the generalized cyclic mean and generalized cyclic spectrum features of the signals are extracted, and the waveform feature matrix of UAV swarm communication in complex multipath environment is established. Finally, the sparse autoencoder deep neural network UAV swarm communication waveform recognition model is established. The simulation results show that the proposed algorithm has strong robustness in the complex environment of Alpha pulse interference, multipath fading and frequency shift, and realizes the recognition of seven kinds of UAV swarm communication waveforms. At the same time, the recognition accuracy of more than 80% can be guaranteed when the signal to noise ratio is -10 dB.

Key words: unmanned aerial vehicle (UAV) swarm, modulation recognition, Alpha pulse interference, sparse autoencoder

CLC Number:

TN911

Ruping ZHAI, Shuheng ZHANG, Jiarong PING. Waveform recognition of unmanned aerial vehicle swarm communication in complex multipath environment[J]. Systems Engineering and Electronics, 2023, 45(10): 3312-3320.

Figures/Tables 19

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

Fig.5

Fig.6

Table 2

Table 3

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Table 4

Fig.13

Table 5

Fig.14

References 21

1	柳征, 王明阳, 姜文利, 等. 一种新的贝叶斯调制分类算法[J]. 电子与信息学报, 2006, 7, 1233- 1237.
	LIU Z , WANG M Y , JIANG W L , et al. A novel Bayesian modulation classification algorithm[J]. Journal of Electronics and Information, 2006, 7, 1233- 1237.
2	李红光, 郭英, 眭萍, 等. 基于时频能量谱纹理特征的跳频调制方式识别[J]. 通信学报, 2019, 40 (10): 20- 29.
	LI H G , GUO Y , SUI P , et al. Identification of frequency hopping modulation mode based on time-frequency energy spectrum texture features[J]. Journal of Communications, 2019, 40 (10): 20- 29.
3	KUMAR Y , SHEORAN M , JAJOO G , et al. Automatic modulation classification based on constellation density using deep learning[J]. IEEE Communications Letters, 2020, 24 (6): 1275- 1278. doi: 10.1109/LCOMM.2020.2980840
4	XIE W W , HU S , YU C , et al. Deep learning in digital modulation recognition using high order cumulants[J]. IEEE Access, 2019, 63760- 63766.
5	郝云飞. 基于深度学习的无人机信号检测与识别技术研究[D]. 长沙: 国防科技大学, 2019: 42-56.
	HAO Y F. Research on detection and recognition technology for UAV signal based on deep learning[D]. Changsha: National University of Defense Technology, 2019: 42-56.
6	王自维, 姚志成, 王海洋, 等. 基于改进LeNet-5模型的无人机遥控信号调制方式识别算法[J]. 火箭军工程大学学报, 2021, 4, 30- 34.
	WANG Z W , YAO Z C , WANG H Y , et al. Recognition algorithm of UAV remote control signal modulation mode based on improved LeNet-5 model[J]. Journal of Rocket Force Engineering University, 2021, 4, 30- 34.
7	TU Y , LIN Y . Deep neural network compression technique towards efficient digital signal modulation recognition in edge device[J]. IEEE Access, 2019, 7, 58113- 58119. doi: 10.1109/ACCESS.2019.2913945
8	WANG D , ZHANG M , LI Z , et al. Modulation format recognition and OSNR estimation using CNN-based deep learning[J]. IEEE Photonics Technology Letters, 2017, 29 (19): 1667- 1670. doi: 10.1109/LPT.2017.2742553
9	ZHANG Z F , WANG C , GAN C Q , et al. Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD[J]. IEEE Trans.on Signal and Information Processing Over Networks, 2019, 5 (3): 469- 478. doi: 10.1109/TSIPN.2019.2900201
10	张天骐, 范聪聪, 葛宛营, 等. 基于ICA和特征提取的MIMO信号调制识别算法[J]. 电子与信息学报, 2020, 42 (9): 2208- 2215.
	ZHANG T Q , FAN C C , GE W Y , et al. A modulation identification algorithm of MIMO signal based on ICA and feature extraction[J]. Journal of Electronics and Information, 2020, 42 (9): 2208- 2215.
11	PENG S L , JIANG H Y , WANG H X , et al. Modulation classification based on signal constellation diagrams and deep learning[J]. IEEE Trans.on Neural Networks and Learning Systems, 2018, 30 (3): 718- 727.
12	张思成, 林云, 涂涯, 等. 基于轻量级深度神经网络的电磁信号调制识别技术[J]. 通信学报, 2020, 41 (11): 12- 21.
	ZHANG S C , LIN Y , TU Y , et al. Electromagnetic signal modulation identification technology based on lightweight deep neural network[J]. Journal of Communications, 2020, 41 (11): 12- 21.
13	李晨, 杨俊安, 刘辉. 基于信息熵和GA-ELM的调制识别算法[J]. 系统工程与电子技术, 2020, 42 (1): 223- 229.
	LI C , YANG J A , LIU H . Modulation identification algorithm based on information entropy and GA-ELM[J]. Systems Engineering and Electronics, 2020, 42 (1): 223- 229.
14	史蕴豪, 许华, 郑万泽, 等. 基于集成学习与特征降维的小样本调制识别方法[J]. 系统工程与电子技术, 2021, 43 (4): 1099- 1109.
	SHI Y H , XU H , ZHENG W Z , et al. Few-sample modulation recognition method based on ensemble learning and feature dimensionality reduction[J]. Systems Engineering and Electro-nics, 2021, 43 (4): 1099- 1109.
15	冯径, 熊鑫立, 蒋磊. 软件通信适配器的调制模式识别算法[J]. 东南大学学报, 2017, 47 (3): 456- 460.
	FENG J , XIONG X L , JIANG L . Modulation pattern recognition algorithm for software communication adapter[J]. Journal of Southeast University, 2017, 47 (3): 456- 460.
16	ETSI TR 38.901. Study on channel model for frequencies from 0.5 to 100 GHz (V17.0.0)[S]. 3GPP, 2022.
17	JAKES W C . Microwave mobile communications[M]. New York: Wiley, 1974.
18	SAMORODNITSKY G , TAQQU M S . Stable non-Gaussian random processes: stochastic models with infinite variance[M]. London: Routledge, 2017.
19	AZZOUZ E , NANDI A K . Automatic modulation recognition of communication signals[M]. BerLin: Springer Science & Business Media, 2013.
20	孔豫京. 基于Alpha稳定分布理论的无人机侦察信道建模与信号调制识别技术研究[D]. 郑州: 解放军信息工程大学, 2015: 52-53.
	KONG Y J. Research on channel modeling and signal modulation recognition for UAV reconnaissance based on Alpha stable distribution theory[D]. Zhengzhou: PLA Information Engineering University, 2015: 52-53.
21	ALI A , YANG Y F . Automatic modulation classification using deep learning based on sparse autoencoders with nonnegativity constraints[J]. IEEE Signal Processing Letters, 2017, 24 (11): 1626- 1630.

调制类型	循环均值	f=0截面的循环谱
MASK	ε=f_c	ε=2f_c
BPSK	无离散峰值	ε=±(2f_c±R_b)
QPSK	无离散峰值	无离散峰值
MFSK	ε=f_c+f_m	ε=±2(f_c+f_m)
MSK	无离散峰值	ε=±(2f_c±R_b/2)

参数	无人机1	无人机2
飞行速度/(m/s)	50.0	50.5
最大多普勒频移/Hz	967	976
TDL-A时延扩展/ns	100	101
TDL-D时延扩展/ns	30	31

SAE网络模型	参数值
输入层	14
隐藏层	(10, 8)
输出层	6
激活函数	Linear, ReLU, Softmax
优化算法	L-BFGS
稀疏系数	0.1
稀疏权重因子	3
权重衰减系数	0.000 1

无人机	飞行速度/(m/s)	最大多普勒频移/Hz	时延扩展/ns
1	50.0	967	100
2	50.5	976	101
3	51.0	986	102
4	51.5	995	103

场景	参数	无人机1	无人机2
所有场景	时延扩展/ns	100	101
场景1	飞行速度/(m/s)	15	15.5
场景1	最大多普勒频移/Hz	290	300
场景2	飞行速度/(m/s)	25	25.5
场景2	最大多普勒频移/Hz	483	493
场景3	飞行速度/(m/s)	50	50.5
场景3	最大多普勒频移/Hz	967	976
场景4	飞行速度/(m/s)	70	70.5
场景4	最大多普勒频移/Hz	1 353	1 363

Waveform recognition of unmanned aerial vehicle swarm communication in complex multipath environment

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 19

References 21

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Xiaogang QI, Yutong ZHOU, Lifang LIU. Evaluation of the reliability of UAV swarm for ground combat missions [J]. Systems Engineering and Electronics, 2023, 45(9): 2971-2978.
[2]	Pei GONG, Tianyun LI, Xinliang ZHANG, Chentao CUN. Modulation recognition for satellite single-mixed signals using joint characteristic parameters [J]. Systems Engineering and Electronics, 2023, 45(2): 589-596.
[3]	Bowei QIN, Lei JIANG, Hua XU, Zisen QI. Modulation recognition algorithm based on residual generation adversarial network [J]. Systems Engineering and Electronics, 2022, 44(6): 2019-2026.
[4]	Xuping GU, Daquan TANG. Hierarchical cooperative navigation of UAV swarm based on federated filtering algorithm [J]. Systems Engineering and Electronics, 2022, 44(3): 967-976.
[5]	Kai SHAO, Miaomiao ZHU, Guangyu WANG. Modulation recognition method based on generative adversarial andconvolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(3): 1036-1043.
[6]	Xingjia YANG, Keqing DUAN, Xiang LI, Wei QI. Resolving range ambiguity for cooperative detection using UAV swarms [J]. Systems Engineering and Electronics, 2022, 44(2): 480-489.
[7]	Xiaowei FU, Jing PAN. Distributed formation control of UAV swarm with dynamic obstacle avoidance [J]. Systems Engineering and Electronics, 2022, 44(2): 529-537.
[8]	Yunhao SHI, Hua XU, Wanze ZHENG, Yinghui LIU. Few-shot modulation recognition method based on ensemble learning and feature dimension reduction [J]. Systems Engineering and Electronics, 2021, 43(4): 1099-1109.
[9]	Zihao SONG, Wei CHENG, Cenxin PENG, Xiaobai LI. Modulation recognition method based on CWD and residual shrinkage network [J]. Systems Engineering and Electronics, 2021, 43(11): 3371-3379.
[10]	Kai LIU, Bin ZHANG, Qinghua HUANG. Modulation recognition algorithm based on TCNN-BiLSTM [J]. Systems Engineering and Electronics, 2020, 42(8): 1841-1849.
[11]	Yang ZHANG, Guangya SI, Yanzheng WANG. Simulation of unmanned aerial vehicle swarm electromagnetic operation concept [J]. Systems Engineering and Electronics, 2020, 42(7): 1510-1518.
[12]	Kai LIU, Mengwei ZHAO, Qinghua HUANG. Multi-h CPM modulation recognition algorithm based on approximate entropy [J]. Systems Engineering and Electronics, 2020, 42(3): 698-703.
[13]	Chen LI, Jun'an YANG, Hui LIU. Modulation recognition algorithm based on information entropy and GA-ELM [J]. Systems Engineering and Electronics, 2020, 42(1): 223-229.
[14]	ZHANG Yang, SI Guangya, WANG Yanzheng. Modeling of cooperative target allocation of the UAV swarm cyberspace attack action [J]. Systems Engineering and Electronics, 2019, 41(9): 2025-2033.
[15]	HAO Yunfei, LIU Zhangmeng, GUO Fucheng, ZHANG Min. Open-set recognition of signal modulation based on generative adversarial networks [J]. Systems Engineering and Electronics, 2019, 41(11): 2619-2624.