同步提取变换去噪的雷达信号调制识别方法

doi:10.12305/j.issn.1001-506X.2024.10.11

Abstract

Abstract:

Due to the insufficient time-frequency focusing ability of the existing Cohen-class time-frequency distribution and low modulation recognition accuracy under low signal to noise ratio (SNR), a grouped convolutional neural network modulation recognition method based on synchronous extracting transform (SET) denoising is proposed. Firstly, SET is used for time-frequency analysis of the radar signals, providing better time-frequency focusing and computational efficiency of time-frequency analysis. Then, the Viterbi algorithm is utilized to search and estimate the instaneous frequency trajectory in the time-frequency coefficient matrix, taking into account the distribution of signal energy intensity and the smoothness of the instaneous frequency trajectory. At the same time, a median filter is applied to remove pulse noise from the obtained instaneous frequency trajectory, and the time-frequency coefficients in the vicinity of the instaneous frequency trajectory are retained to achieve time-frequency image denoising. Finally, the denoised time-frequency images are sent to a grouped convolution neural network with residual connections for feature extraction and modulation recognition. The experimental results demonstrate that, when the SNR is -12 dB, the denoised SET time-frequency images have good time-frequency focusing, and the modulation recognition accuracy is improved by 13.69% compared to the recognition accuracy without denoising. The proposed radar signal modulation recognition method exhibits excellent recognition performance for various complex modulation types of signals under low SNR conditions.

Key words: radar signal modulation recognition, synchro-extracting transform, instaneous frequency estimation, denoising

CLC Number:

TN957.51

Zhian DENG, Zhiguo WANG, Sheng'ao WANG, Weijian SI. Radar signal modulation recognition method based on synchro-extracting transform denoising[J]. Systems Engineering and Electronics, 2024, 46(10): 3334-3346.

Figures/Tables 22

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References 31

17	YUAN S B , WU B , LI P . Intra-pulse modulation classification of radar emitter signals based on a 1-D selective kernel convolutional neural network[J]. Remote Sensing, 2021, 13 (14): 2799. doi: 10.3390/rs13142799
18	LI D J , YANG R J , LI X B , et al. Radar signal modulation recognition based on deep joint learning[J]. IEEE Access, 2020, 8, 48515- 48528. doi: 10.1109/ACCESS.2020.2978875
19	LIAO Y P , JIANG F , WANG J L . Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network[J]. The Journal of Supercomputing, 2023, 79 (6): 6422- 6451. doi: 10.1007/s11227-022-04902-9
20	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
21	郭立民, 寇韵涵, 陈涛, 等. 基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别[J]. 电子与信息学报, 2018, 40 (4): 875- 881.
	GUO L M , KOU Y H , CHEN T , et al. Low probability of intercept radar signal recognition based on stacked sparse auto-encoder[J]. Journal of Electronics & Information Technology, 2018, 40 (4): 875- 881.
22	邵凯, 朱苗苗, 王光宇. 基于生成对抗与卷积神经网络的调制识别方法[J]. 系统工程与电子技术, 2022, 44 (3): 1036- 1043.
	SHAO K , ZHU M M , WANG G Y . Modulation recognition method based on generative adversarial and convolutional neural network[J]. Systems Engineering and Electronics, 2022, 44 (3): 1036- 1043.
23	OBERLIN T, MEIGNEN S, PERRIER V. The Fourier-based synchrosqueezing transform[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2014: 315-319.
24	YU G , YU M J , XU C Y . Synchroextracting transform[J]. IEEE Trans.on Industrial Electronics, 2017, 64 (10): 8042- 8054. doi: 10.1109/TIE.2017.2696503
25	MAO W T , CHEN J X , LIANG X H , et al. A new online detection approach for rolling bearing incipient fault via self-adaptive deep feature matching[J]. IEEE Trans.on Instrumentation and Measurement, 2019, 69 (2): 443- 456.
1	ISMAIL G U L , ISLN E . Randomized low rank and sparse decomposition based receiver search strategy for electronic support receivers[J]. IEEE Trans.on Aerospace and Electronic Systems, 2022, 59 (2): 1392- 1399.
2	XU X S , BI D P , PAN J F , et al. An end-to-end deep learning approach for state recognition of multifunction radars[J]. Sensors, 2022, 22 (13): 4980. doi: 10.3390/s22134980
3	PARK B, AHN J M. Intra-pulse modulation recognition using pulse description words and complex waveforms[C]//Proc. of the International Conference on Information and Communication Technology Convergence, 2017: 555-560.
4	ZHU M T , LI Y J , WANG S F . Model-based time series clustering and interpulse modulation parameter estimation of multifunction radar pulse sequences[J]. IEEE Trans.on Aerospace and Electronic Systems, 2021, 57 (6): 3673- 3690. doi: 10.1109/TAES.2021.3082660
5	ZHANG X L , ZHANG J Z , LUO T Z , et al. Radar signal intrapulse modulation recognition based on a denoising-guided disentangled network[J]. Remote Sensing, 2022, 14 (5): 1252. doi: 10.3390/rs14051252
6	XU J L , SU W , ZHOU M C . Likelihood-ratio approaches to automatic modulation classification[J]. IEEE Trans.on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2010, 41 (4): 455- 469.
7	HAMEED F , DOBRE O A , POPESCU D C . On the likelihood-based approach to modulation classification[J]. IEEE Trans.on Wireless Communications, 2009, 8 (12): 5884- 5892. doi: 10.1109/TWC.2009.12.080883
8	RAMEZANI-KEBRYA A , KIM I M , KIM D I , et al. Likelihood-based modulation classification for multiple-antenna receiver[J]. IEEE Trans.on Communications, 2013, 61 (9): 3816- 3829. doi: 10.1109/TCOMM.2013.073113.121001
9	ZHANG J W , CABRIC D , WANG F G , et al. Cooperative modulation classification for multipath fading channels via expectation-maximization[J]. IEEE Trans.on Wireless Communications, 2017, 16 (10): 6698- 6711. doi: 10.1109/TWC.2017.2728530
10	李正权, 林媛, 李梦雅, 等. 基于判别式受限玻尔兹曼机的数字调制识别[J]. 通信学报, 2021, 42 (2): 81- 91.
	LI Z Q , LIN Y , LI M Y , et al. Digital modulation recognition based on discriminative restricted Boltzmann machine[J]. Journal on Communications, 2021, 42 (2): 81- 91.
11	RU X H , LIU Z , JIANG W L , et al. Recognition performance analysis of instaneous phase and its transformed features for radar emitter identification[J]. IET Radar, Sonar & Navigation, 2016, 10 (5): 945- 952.
12	CHENG Y Q , GUO M R , GUO L M . Radar signal recognition exploiting information geometry and support vector machine[J]. IET Signal Processing, 2023, 17 (1): e12167.
13	ASLAM M W , ZHU Z C , NANDI A K . Automatic modulation classification using combination of genetic programming and KNN[J]. IEEE Trans.on Wireless Communications, 2012, 11 (8): 2742- 2750.
14	CHEN K Y , ZHU L Z , CHEN S , et al. Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution[J]. Measurement, 2021, 185, 109945. doi: 10.1016/j.measurement.2021.109945
15	WEI S J , QU Q Z , SU H , et al. Intra-pulse modulation radar signal recognition based on CLDN network[J]. IET Radar, Sonar & Navigation, 2020, 14 (6): 803- 810.
16	ZHU Z G , JI H B , ZHANG W B , et al. Complex convolutional neural network for signal representation and its application to radar emitter recognition[J]. IEEE Communications Letters, 2023, 27 (3): 856- 860. doi: 10.1109/LCOMM.2023.3234464
26	SI W J , WAN C X , DENG Z A . Intra-pulse modulation recognition of dual-component radar signals based on deep convolutional neural network[J]. IEEE Communications Letters, 2021, 25 (10): 3305- 3309.
27	YU H H , YAN X P , LIU S K , et al. Radar emitter multi-label recognition based on residual network[J]. Defence Technology, 2022, 18 (3): 410- 417.
28	HOU C B , FU D Y , HUA L J , et al. The recognition of multi-components signals based on semantic segmentation[J]. Wireless Networks, 2023, 29 (1): 147- 160.
29	秦鑫, 黄洁, 查雄, 等. 基于扩张残差网络的雷达辐射源信号识别[J]. 电子学报, 2020, 48 (3): 456- 462.
	QIN X , HUANG J , ZHA X , et al. Radar emitter signal recognition based on dilated residual network[J]. Acta Electronica Sinica, 2020, 48 (3): 456- 462.
30	QU Z Y , MAO X J , DENG Z A . Radar signal intra-pulse modulation recognition based on convolutional neural network[J]. IEEE Access, 2018, 6, 43874- 43884.
31	QU Z Y , WANG W Y , HOU C B , et al. Radar signal intra-pulse modulation recognition based on convolutional denoising autoencoder and deep convolutional neural network[J]. IEEE Access, 2019, 7, 112339- 112347.

信号长度N	方法
信号长度N	SET	CWD	SPWVD
512	44.7	155.5	179.1
768	93.1	343.1	385.9
1 024	176.3	673.5	747.1

信号调制类型	训练集和验证集参数	测试集参数
BPSK	载频: 5~40 MHz	载频: 5~45 MHz
	Baker码长度: {5、7、11、13}	Baker码长度: {5、7、11}
	码元宽度: 500~800 ns	码元宽度: 400~700 ns
	相位循环次数: 2~5	相位循环次数: 4~6
CW	载频: 5~40 MHz	载频: 5~45 MHz
CW	脉宽: 6.4~13.6 μs	脉宽: 4.8~11.2 μs
DLFM、EQFM、LFM、SFM	中心频率: 5~30 MHz	中心频率: 10~35 MHz
	带宽: 10~20 MHz	带宽: 15~30 MHz
	脉宽: 6.4~13.6 μs	脉宽: 4.8~11.2 μs
2FSK	2个载频: 5~40 MHz	2个载频: 5~45 MHz
	码元宽度: 1.5~2 μs	码元宽度: 1.2~1.6 μs
	码元数量: 4~8	码元数量: 6~12
4FSK	4个载频: 5~40 MHz	4个载频: 5~45 MHz
	码元宽度: 1.5~2 μs	码元宽度: 1.2~1.6 μs
	码元数量: 8~12	码元数量: 10~16
P1码、P2码、P3码、P4码、Frank	载频: 5~40 MHz	载频: 5~45 MHz
	相位跳变数: (5~8)²	相位跳变数: (5~8)²
	码元宽度: 100~600 ns	码元宽度: 200~700 ns

分组数d	模型参数量(M)	识别精度(SNR=-12 dB)/%
2	1.309 29	65.35
4	0.866 93	66.60
8	0.645 74	65.61

Stage	模型参数量(M)	识别精度(SNR=-12 dB)/%
[2, 2, 0, 1]	0.305 26	62.46
[2, 2, 2, 1]	0.617 07	64.57
[2, 2, 4, 1]	0.866 93	66.60
[2, 2, 6, 1]	1.116 78	61.22

[1]	Yonghua ZENG, Junyao MA, Chaoyan HUANG, Zhihui MAO, Tingting WU. Color image denoising method combining prue quaternion and dictionary learning [J]. Systems Engineering and Electronics, 2023, 45(2): 373-378.
[2]	Yuzhao MA, Kui LIU, Yanfeng ZHANG, Shuai FENG, Xinglong XIONG. Laser radar signal denoising algorithm based on CEEMD combined with improved wavelet threshold [J]. Systems Engineering and Electronics, 2023, 45(1): 93-100.
[3]	Ziyan LIU, Shanshan MA, Jing LIANG, Mingcheng ZHU, Lei YUAN. Attention mechanism based CNN channel estimation algorithm in millimeter-wave massive MIMO system [J]. Systems Engineering and Electronics, 2022, 44(1): 307-312.
[4]	Pengyu CAO, Chengzhi YANG, Limeng SHI, Hongchao WU. LPI radar signal enhancement based on DAE-GAN network [J]. Systems Engineering and Electronics, 2021, 43(9): 2493-2500.
[5]	Kai SHAO, Liancheng CHEN, Yin LIU. Learning and estimation of high mobility Jakes channel [J]. Systems Engineering and Electronics, 2021, 43(4): 1119-1125.
[6]	Pei LIU, Jian JIA, Li CHEN, Ying AN. Image denoising algorithm based on boosting high order non-convex total variation model [J]. Systems Engineering and Electronics, 2020, 42(3): 557-567.
[7]	WANG Caiyun, ZHAO Huanyue, WANG Jianing, LI Xiaofei, HUANG Panpan. SAR image denoising via fast weighted nuclear norm minimization [J]. Systems Engineering and Electronics, 2019, 41(7): 1504-1508.
[8]	CHEN Haiyan, DU Jinghan, ZHANG Weining. Monitoring data repairing method based on deep denoising auto-encoder network [J]. Systems Engineering and Electronics, 2018, 40(2): 435-440.
[9]	HUANG Yingkun, JIN Weidong, YU Zhibin, WU Yunpu. Radar emitter signal recognition based on deep learning and ensemble learning [J]. Systems Engineering and Electronics, 2018, 40(11): 2420-.
[10]	WANG Caiyun, HU Yunkan, WU Shuxia. Shearlet domain SAR image denoising method based on Bayesian model [J]. Systems Engineering and Electronics, 2017, 39(6): 1250-1255.
[11]	LU Zhizhong, ZHOU Ying, HUANG Yu. Research on correlation in spatial domain to eliminate the co-channel interference of the X-band marine radar [J]. Systems Engineering and Electronics, 2017, 39(4): 758-767.
[12]	REN Jun, HU Xiaofeng, ZHU Feng. Effectiveness prediction of weapon equipment systemofsystems based on deep learning feature transfer#br# [J]. Systems Engineering and Electronics, 2017, 39(12): 2745-2749.
[13]	HU Yue, ZHONG Chongxiao, CAO Mengyu, ZHAO Kuangshi. Augmented Lagrangian multiplier based fast higher degree total variation image denoising algorithm#br# [J]. Systems Engineering and Electronics, 2017, 39(12): 2831-2839.
[14]	LIU Shu-jun, WU Guo-qing, ZHANG Xin-zheng, SHEN Xiao-dong, LI Yong-ming. SAR image denoising via linear minimum meansquare error estimation [J]. Systems Engineering and Electronics, 2016, 38(4): 785-791.
[15]	GUO Xiao-song, ZHANG Dong-fang, XUE Hai-jian, ZHOU Zhao-fa. Whole attitude to find north based on wavelet denoising and total least squares [J]. Systems Engineering and Electronics, 2016, 38(2): 362-367.

Radar signal modulation recognition method based on synchro-extracting transform denoising

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