基于稀疏表示分类的雷达欺骗干扰识别方法

doi:10.12305/j.issn.1001-506X.2022.09.12

Abstract

Abstract:

In order to deal with a variety of deception jamming signals based on digital radio frequency memory jammer, this paper presents an algorithm for deception jamming recognition based on sparse representation classification. Firstly, the wavelet packet decomposition and reconstruction are used to divide the signal into different frequency bands, and the feature matrix is constructed by features extracted from the third-order cumulant slice. Then, singular value decomposition is used to reduce the dimension of the feature matrix and extracts the main components. Next, the classification results on each frequency band are obtained by sparse representation classification method, and finally the results are integrated by decision fusion. It has been verified that this method has good anti-noise performance and can effectively identify several common deception jamming signals.When the signal noise ratio is 0 dB, the average recognition rate of deception jamming is more than 90%. Compared with other methods of deception jamming recognition, the superiority of proposed method can be proved.

Key words: active jamming identification, deception jamming, wavelet packet decomposition, third-order cumulant, singular value decomposition, sparse representation

CLC Number:

TN974

Hongping ZHOU, Minghui MA, Ruowu WU, Xiong XU, Zhongyi GUO. Deception jamming recognition of radar based on sparse representation classification[J]. Systems Engineering and Electronics, 2022, 44(9): 2791-2799.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 1

Table 2

Fig.8

References 35

1	KIM J , CHO D H , LEE W C , et al. Optimal target assignment with seamless handovers for networked radars[J]. Sensors, 2019, 19 (20): 4555. doi: 10.3390/s19204555
2	PAN Q , NARAYANAN R M . Delay-modulated RF tag system concept using ultrawideband noise radar waveforms[J]. International Journal of Distributed Sensor Networks, 2011, 7 (1): 156582. doi: 10.1155/2011/156582
3	NUSS B, SIT L, FENNEL M, et al. MIMO OFDM radar system for drone detection[C]//Proc. of the 18th IEEE International Radar Symposium, 2017.
4	BI W , ZHAO Y H , SHEN R Q , et al. Multi-frequency GPR data fusion and its application in NDT[J]. NDT & E International, 2020, 115, 102289.
5	NA H , LEE J I . Optimal arrangement of missile defense systems considering kill probability[J]. IEEE Trans.on Aerospace and Electronic Systems, 2019, 56 (2): 972- 983.
6	LV P , WEI G H , CUI W . Vector motion parameter estimation for an approaching missile based on an extended-target model[J]. IEEE Trans.on Antennas and Propagation, 2018, 66 (10): 6464- 5474.
7	BURCIU Z , ABRAMOWICZ-GERIGK T , PRZYBYL W , et al. The impact of the improved search object detection on the SAR action success probability in maritime transport[J]. Sensors, 2020, 20 (14): 3962. doi: 10.3390/s20143962
8	QIN R , FU X J , DONG J , et al. A semi-greedy neural network CAE-HL-CNN for SAR target recognition with limited training data[J]. International Journal of Remote Sensing, 2020, 41 (20): 7889- 7911. doi: 10.1080/01431161.2020.1766149
9	YANG J, GUO X X, LI Y J. Design of a novel DRFM jamming system based on AFB-SFB[C]//Proc. of the IET International Radar Conference, 2013.
10	孙闽红, 唐斌. 距离-速度同步拖引欺骗干扰的频谱特征分析[J]. 系统工程与电子技术, 2009, 31 (1): 83- 85. doi: 10.3321/j.issn:1001-506X.2009.01.021
	SUN M H , TANG B . Analysis of the frequency spectrum of a simultaneous range-gate-pull-offand velocity-gate-pull-off jamming signal[J]. Systems Engineering and Electronics, 2009, 31 (1): 83- 85. doi: 10.3321/j.issn:1001-506X.2009.01.021
11	OLIVIER K , CILLIERS J E , PLESSIS M D . Design and performance of wideband DRFM for radar test and evaluation[J]. Electronics Letters, 2011, 47 (14): 824- 825. doi: 10.1049/el.2011.0362
12	TAO B R . An ECCM model technical development trends to the demands of the future EW combat[J]. IEEE AES Systems Magazine, 1994, 9 (6): 12- 16. doi: 10.1109/62.286787
13	李建勋, 唐斌, 吕强. 双谱特征提取在欺骗式干扰方式识别中应用研究[J]. 电子科技大学学报, 2009, 38 (3): 329- 332. doi: 10.3969/j.issn.1001-0548.2009.03.003
	LI J X , TANG B , LYU Q . Bispectrum feature extraction used in deceptive jamming modes recognition[J]. Journal of University of Electronic Science and Technology of China, 2009, 38 (3): 329- 332. doi: 10.3969/j.issn.1001-0548.2009.03.003
14	史忠亚, 吴华, 沈文迪, 等. 基于双域特征的雷达欺骗干扰样式识别方法[J]. 火力与指挥控制, 2018, 43 (1): 136- 140. doi: 10.3969/j.issn.1002-0640.2018.01.028
	SHI Z Y , WU H , SHEN W D , et al. Method of radar deceptive jamming modes recognition based on two domains features[J]. Fire Control & Command Control, 2018, 43 (1): 136- 140. doi: 10.3969/j.issn.1002-0640.2018.01.028
15	TIAN X , TANG B , GUI G . Product spectrum matrix feature extraction and recognition of radar deception jamming[J]. International Journal of Electronics, 2013, 100 (12): 1621- 1629. doi: 10.1080/00207217.2012.743095
16	李艳莉, 田晓. 基于积谱矩阵局部二值模式的欺骗干扰识别[J]. 电讯技术, 2015, 55 (4): 441- 446. doi: 10.3969/j.issn.1001-893x.2015.04.016
	LI Y L , TIAN X . Active deception jamming recognition based on local binary pattern of product spectrum matrix[J]. Telecommunication Engineering, 2015, 55 (4): 441- 446. doi: 10.3969/j.issn.1001-893x.2015.04.016
17	卢云龙, 李明, 闫琐. 利用调频率匹配的DRFM欺骗干扰检测方法[J]. 西安电子科技大学学报, 2014, 41 (5): 67- 73. doi: 10.3969/j.issn.1001-2400.2014.05.012
	LU Y L , LI M , YAN S . Method for detecting DRFM deception jamming based on LFM rate matching[J]. Journal of Xidian University, 2014, 41 (5): 67- 73. doi: 10.3969/j.issn.1001-2400.2014.05.012
18	杨兴宇, 阮怀林. 基于局部二值模式特征的新型干扰识别算法[J]. 计算机工程, 2018, 44 (7): 291- 296.
	YANG X Y , RUAN H L . New jamming identification algorithm based on local binary pattern feature[J]. Computer Engineering, 2018, 44 (7): 291- 296.
19	曲志昱, 毛校洁, 侯长波. 基于奇异值熵和分形维数的雷达信号识别[J]. 系统工程与电子技术, 2018, 40 (2): 303- 307.
	QU Z Y , MAO X J , HOU C B . Radar signal recognition based on singular value entropy and fractal dimension[J]. Systems Engineering and Electronics, 2018, 40 (2): 303- 307.
20	刘立芳, 杨海霞, 齐小刚. 基于线性判别分析的时频域特征提取算法[J]. 系统工程与电子技术, 2019, 41 (10): 2184- 2190. doi: 10.3969/j.issn.1001-506X.2019.10.05
	LIU L F , YANG H X , QI X G . Time-frequency domain feature extraction algorithm based on linear discriminant analysis[J]. Systems Engineering and Electronics, 2019, 41 (10): 2184- 2190. doi: 10.3969/j.issn.1001-506X.2019.10.05
21	ZHOU H P , DONG C C , WU R W , et al. Feature fusion based on bayesian decision theory for radar deception jamming recognition[J]. IEEE Access, 2021, 9, 16296- 16304. doi: 10.1109/ACCESS.2021.3052506
22	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
23	SHAO G Q , 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
24	QIN Y, YANG J, ZHU M T, et al. Fast recognition of pull-off jamming using LSTM[C]//Proc. of the IEEE International Conference on Signal, Information and Data Processing, 2019.
25	NI X , WANG H L , MENG F , et al. LPI radar waveform recognition based on multi-resolution deep feature fusion[J]. IEEE Access, 2021, 9, 26138- 26146. doi: 10.1109/ACCESS.2021.3058305
26	LI M , REN Q H , WU J L . Interference classification and identification of TDCS based on improved convolutional neural network[J]. Journal of Physics: Conference Series, 2020, 1651 (1): 012155. doi: 10.1088/1742-6596/1651/1/012155
27	熊英, 刘旻, 唐斌, 等. 基于盒维数与L-Z复杂度的雷达复合干扰特征提取方法[J]. 数据采集与处理, 2008, 23 (6): 663- 667. doi: 10.3969/j.issn.1004-9037.2008.06.006
	XIONG Y , LIU M , TANG B , et al. Feature-extracting method for radar compound jamming signal based on box dimension and L-Z complexity[J]. Journal of Data Acquisition and Processing, 2008, 23 (6): 663- 667. doi: 10.3969/j.issn.1004-9037.2008.06.006
28	DU C J , TANG B . Novel unconventional-active-jamming recognition method for wide-band radars based on visibility graphs[J]. Sensors, 2019, 19 (10): 2344. doi: 10.3390/s19102344
29	原慧, 王春阳, 安磊, 等. 基于信号重构的频谱弥散干扰抑制方法[J]. 系统工程与电子技术, 2017, 39 (5): 960- 967.
	YUAN H , WANG C Y , AN L , et al. Smeared spectrum jamming suppression method based on signal reconstruction[J]. Systems Engineering and Electronics, 2017, 39 (5): 960- 967.
30	隋鉴, 李国林, 王凌. 基于噪声卷积的灵巧噪声伪码调相引信干扰[J]. 系统工程与电子技术, 2014, 36 (10): 1924- 1928. doi: 10.3969/j.issn.1001-506X.2014.10.06
	SUI J , LI G L , WANG L . Smart noise jamming based on noise convolution modulation countering pseudo-random code phase modulation fuze[J]. Systems Engineering and Electronics, 2014, 36 (10): 1924- 1928. doi: 10.3969/j.issn.1001-506X.2014.10.06
31	杨兴宇, 阮怀林. 基于双谱分析的雷达有源欺骗干扰识别[J]. 探测与控制学报, 2018, 40 (2): 122- 127.
	YANG X Y , RUAN H L . Active deception jamming identification method based on bispectrum analysis[J]. Journal of Detection & Control, 2018, 40 (2): 122- 127.
32	WEI D X, ZHANG S N, CHEN S, et al. Research on deception jamming of chaotic composite short-range detection system based on bispectral analysis and genetic algorithm-back propagation[J]. International Journal of Distributed Sensor Networks, 2019, 15(5): 1550147719847444.
33	卢云龙, 李明, 陈洪猛, 等. 基于奇异谱分析的抗数字射频存储距离波门拖引干扰[J]. 电子与信息学报, 2016, 38 (3): 600- 606.
	LU Y L , LI M , CHEN H M , et al. Countering DRFM range gate pull-off jamming based on singular spectrum analysis[J]. Journal of Electronics & Information Technology, 2016, 38 (3): 600- 606.
34	SALARY R R , LOMBARDI J P , WEERAWARNE D L , et al. A sparse representation classification approach for near real-time, physics-based functional monitoring of aerosol jet-fabricated electronics[J]. Journal of Manufacturing Science and Engineering, 2020, 142 (8): 081007. doi: 10.1115/1.4047045
35	WRIGHT J , YANG A Y , GANESH A , et al. Robust face recognition via sparse representation[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2009, 31 (2): 210- 227. doi: 10.1109/TPAMI.2008.79

距离拖引速率/(m/s)	信噪比/dB
距离拖引速率/(m/s)	0	1	2	3	4	5	6	7	8
600	0.916	0.935	0.939	0.942	0.960	0.971	0.966	0.978	0.983
800	0.930	0.930	0.942	0.945	0.970	0.967	0.974	0.982	0.991
1 000	0.944	0.960	0.968	0.967	0.982	0.984	0.980	0.988	0.990
1 200	0.934	0.942	0.936	0.965	0.961	0.971	0.972	0.981	0.983

速度拖引速率/ (kHz/s)	信噪比/dB
速度拖引速率/ (kHz/s)	0	1	2	3	4	5	6	7	8
40	0.904	0.915	0.938	0.963	0.957	0.964	0.98	0.981	0.986
50	0.944	0.960	0.968	0.967	0.982	0.984	0.98	0.988	0.990
60	0.917	0.944	0.949	0.963	0.968	0.978	0.97	0.971	0.988

[1]	Yanling SHI, Lei WANG, Junhao LI. CFAR detection for small targets on sea surface based on singular value decomposition in projection space [J]. Systems Engineering and Electronics, 2022, 44(2): 512-519.
[2]	Shanxue CHEN, Xinxin WANG. Joint sparse representation hyperspectral image classification based on spatial preprocessing [J]. Systems Engineering and Electronics, 2021, 43(9): 2422-2429.
[3]	Fei YU, Yun YU, Lihui ZHOU, Chunguang PENG. Hyperparameter-free sparse signal direction-of-arrival estimation method with single-snapshot [J]. Systems Engineering and Electronics, 2021, 43(4): 894-900.
[4]	Guisheng WANG, Guoce HUANG, Yequn WANG, Shufu DONG, Qinghua REN, Shuai WEI. Anti-interference method with intelligence for transform domain communication based on cognitive-engine [J]. Systems Engineering and Electronics, 2021, 43(1): 223-231.
[5]	Yuzhao MA, Qiangqiang WANG, Ruisong WANG, Xinglong XIONG. Optical fiber perimeter vibration signal recognition based on SVD and MPSO-SVM [J]. Systems Engineering and Electronics, 2020, 42(8): 1652-1661.
[6]	Tianqi HUANG, Buhong WANG, Jiwei TIAN. False target deception jamming optimized method for centralized netted radar [J]. Systems Engineering and Electronics, 2020, 42(7): 1484-1490.
[7]	Xiaoqiang LI, Jianfeng CHEN, Weijie TAN, Yang WEN, Rongrong ZHANG. Gridless DOA estimation method for monostatic MIMO array based on covariance matrix reconstruction [J]. Systems Engineering and Electronics, 2020, 42(5): 969-977.
[8]	Shanxue CHEN, Wenwen CHEN. Joint sparse representation of hyperspectral image classification based on secondary dictionary [J]. Systems Engineering and Electronics, 2020, 42(3): 550-556.
[9]	Hui ZHAO, Lufa FANG, Tianqi ZHANG, Zhiwei LI, Xianming XU. Image compressive sensing reconstruction via group sparse representation and weighted total variation [J]. Systems Engineering and Electronics, 2020, 42(10): 2172-2180.
[10]	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.
[11]	TAN Weijie, FENG Xi’an. Sparsity-based two dimensional direction-finding method for parallel co-prime arrays [J]. Systems Engineering and Electronics, 2019, 41(5): 937-943.
[12]	ZHOU Zhiwen, HUANG Gaoming, WANG Xuebao, MAN Xin. Specific emitter identification based on joint collaborative represenation [J]. Systems Engineering and Electronics, 2019, 41(4): 724-729.
[13]	YANG Lin, ZHANG Xiangyu, LI Lin, WANG Guohong. Research on range-velocity deception jamming recognition technology based on time-space-frequency feature fusion [J]. Systems Engineering and Electronics, 2019, 41(12): 2684-2691.
[14]	ZONG Jingjing, QIU Tianshuang. Characteristic analysis for the l-1 norm of sparse coefficients in sparse representation [J]. Systems Engineering and Electronics, 2019, 41(12): 2692-2696.
[15]	CHEN Chen, WEI Zhonghao, XU Zhilin, ZHANG Bingchen. High-precision wide angle SAR imaging method based on sparse representation of Gaussian dictionary atoms [J]. Systems Engineering and Electronics, 2019, 41(11): 2471-2478.

Deception jamming recognition of radar based on sparse representation classification

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 35

Related Articles 15

Recommended Articles

Metrics

Comments