干扰效果在线评估参数筛选与特征表示方法

doi:10.3969/j.issn.1001-506X.2020.12.11

Abstract

Abstract:

In view of parameter redundancy and unclear signal change degree in the online evaluation of jamming effect by radar reconnaissance signals, a parameter selection and feature representation method of radar jamming effect online evaluation is proposed. Firstly, for multi-function maritime radar signals, information entropy is used to screen wildly varying parameters. The box dimension and Pearson correlation coefficients are then used to extract the signal variation characteristics. Finally, the support vector machine is used to evaluate the jamming effect of unknown threats. Simulation experiments show that the new method evaluation accuracy is higher than 92%, when the deviation error of the signal parameter is less than 10%, which significantly improves the reliability of online evaluation of radar jamming effect.

Key words: jamming effect, online evaluation, information entropy, box dimension, Pearson correlation coefficient

CLC Number:

TN974

Zhenshuo LEI, Songtao LIU, Yang GE, Zhenming WEN. Parameter selection and feature representation method of jamming effect online evaluation[J]. Systems Engineering and Electronics, 2020, 42(12): 2755-2760.

Figures/Tables 6

Fig.1

Table 1

Table 2

Fig.2

Fig.3

Fig.4

References 27

1	REN Y , LI B , WANG H , et al. A novel cognitive jamming architecture for heterogeneous cognitive electronic warfare networks[M]. Singapore: Springer, 2020.
2	GUO Y Q , WU Y M , LIU H . Construction of waveform library in cognitive radar[J]. Polish Maritime Research, 2017, 24 (s2): 22- 29. doi: 10.1515/pomr-2017-0060
3	KWON G , PARK J Y , HWANG K C . Design of a subarray configuration for multifunction radars using a nested optimization scheme[J]. Electromagnetics, 2016, 36 (4): 276- 285. doi: 10.1080/02726343.2016.1158617
4	SAMEER A. Cognitive electronic warfare system[C]//Proc.of the Cognitive Radio Network, 2016.
5	DABCEVIC K , MUGHAL M O , MARCENARO L , et al. Cognitive radio as the facilitator for advanced communications electronic warfare solutions[J]. Journal ofa Signal Processing Systems, 2016, 83 (1): 29- 44.
6	DARPA. Behavioral learning for adaptive electronic warfare (BLADE) program homepage[EB/OL]. [2020-02-26]. http://www.darpa.mil.
7	AIR FORCE. Cognitive jammer[EB/OL]. [2020-02-26]. https://www.fbo.gov.
8	DARPA. Adaptive radar countermeasures (ARC) program homepage[EB/OL]. [2020-02-26]. http://www.darpa.mil.
9	邢强, 贾鑫, 朱卫纲, 等. 基于干扰方的雷达在线干扰效果评估[J]. 电子信息对抗技术, 2018, 33 (6): 57- 62.
	XING Q , JIA X , ZHU W G , et al. Online jamming effect evaluation of radar based on jammer[J]. Electronic Information Countermeasures Technology, 2018, 33 (6): 57- 62.
10	安红, 杨莉, 高由兵, 等. 基于作战应用的相控阵雷达干扰效果评估方法初探[J]. 电子信息对抗技术, 2014, 29 (3): 42- 46.
	AN H , YANG L , GAO Y B , et al. Preliminary exploration of the phased array radar jamming effect evaluation method based on combat application[J]. Electronic Information Countermea-sures Technology, 2014, 29 (3): 42- 46.
11	赵耀东, 徐旺. 一种基于雷达状态变化的干扰效果在线评估方法[J]. 电子信息对抗技术, 2016, 31 (3): 42- 46.
	ZHAO Y D , XU W . An online method for evaluating jamming effect based on radar state change[J]. Electronic Information Countermeasures Technology, 2016, 31 (3): 42- 46.
12	翁鑫锦.基于机器学习的雷达干扰效能评估[D].成都:电子科技大学, 2019.
	WENG X J. Evaluation of radar jamming effectiveness based on machine learning[D]. Chengdu: University of Electronic Science and Technology of China, 2019.
13	ZHANG W , GAO Y B , ZHENG K . Radar working-state identification using the hidden Markov model[J]. The Journal of Engineering, 2019, 2019 (21): 7632- 7635. doi: 10.1049/joe.2019.0649
14	CUI R, PAN J F, ZHU J. Research on jamming effect evaluation method of AN/TPY-2 radar using set pair approach degree[C]//Proc.of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019.
15	MARTINEZ D L , BRUSONI S . Cognitive flexibility and adaptive decision-making evidence from a laboratory study of expert decision makers[J]. Strategic Management Journal, 2018, 39 (4): 1031- 1058. doi: 10.1002/smj.2774
16	ZHU M T , LI Y J , PAN Z S , et al. Automatic modulation recognition of compound signals using a deep multi-label classifier: a case study with radar jamming signals[J]. Signal Processing, 2020, 169, 107393. doi: 10.1016/j.sigpro.2019.107393
17	YU H L , ZHANG J , ZHANG L R , et al. Polarimetric multiple-radar architectures with distributed antennas for discriminating between radar targets and deception jamming[J]. Digital Signal Processing, 2019, 90, 46- 53. doi: 10.1016/j.dsp.2019.03.012
18	ZHOU B L , LI R F , LIU W J , et al. A BSS-based space-time multi-channel algorithm for complex-jamming suppression[J]. Digital Signal Processing, 2019, 87, 86- 103. doi: 10.1016/j.dsp.2019.01.007
19	SHI J T , LIU X , YANG Y H , et al. Comments on "deceptive jamming suppression with frequency diverse MIMO radar"[J]. Signal Processing, 2019, 158, 1- 3. doi: 10.1016/j.sigpro.2018.12.013
20	LI D D , WANG Z , CAO C J , et al. Information entropy based sample reduction for support vector data description[J]. Applied Soft Computing, 2018, 71, 1153- 1160. doi: 10.1016/j.asoc.2018.02.053
21	THUY N N , WONGTHANAVASU S . On reduction of attri-butes in inconsistent decision tables based on information entropies and stripped quotient sets[J]. Expert Systems With Applications, 2019, 137, 308- 323. doi: 10.1016/j.eswa.2019.06.071
22	DAI M F , SUN Y Q , SHAO S X , et al. Retraction note: modified box dimension and average weighted receiving time on the weighted fractal networks[J]. Scientific Reports, 2020, 10 (1): 6292. doi: 10.1038/s41598-020-63244-9
23	AFYOUNI S , SMITH S M , NICHOLS T E . Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation[J]. Neuroimage, 2019, 199, 609- 625. doi: 10.1016/j.neuroimage.2019.05.011
24	CHEN Y T , XIONG J , XU W H , et al. A novel online incremental and decremental learning algorithm based on variable support vector machine[J]. Cluster Computing, 2019, 22 (3): 7435- 7445.
25	SUN F F , XU P H , DING X M . Multi-core SVM optimized visual word package model for garment style classification[J]. Cluster Computing, 2018, 22 (4): 4141- 4147.
26	SKOLNIK M . Radar handbook[M]. 3rd ed New York: McGraw-Hill, 2008: 246- 252.
27	SHIEH C S , LIN C T . A vector neural network for emitter identification[J]. IEEE Trans.on Antennas & Propagation, 2002, 50 (8): 1120- 1127.

频率/GHz	峰值功率/kW	重频/kHz	脉宽/μs	波束偏移/(°)	波束停留时间/s	带宽/MHz	脉压比	占空比/%
2.9~3.1	10~60	2~20	1~200	0~1.5	1~5	0.2~10	1~200	0.1~10

序号	干扰类型	频率/GHz	峰值功率/kW	重频/kHz	脉宽/μs	波束偏移/(°)	波束停留时间/s	带宽/MHz	脉压比	占空比/%	干扰效果
1	压制式	2.922	20.387	4.107	33.458	0.770	1.764	1.066	35.671	1.374	0
2	压制式	2.912	20.791	5.043	44.538	0.366	1.110	1.098	48.706	2.249	0
3	压制式	2.996	24.610	6.846	70.127	0.626	1.582	2.835	199.156	4.799	1
4	压制式	3.026	47.271	14.223	14.574	0.639	1.636	5.583	81.409	2.076	2
5	压制式	3.082	53.048	10.071	77.496	0.197	2.000	1.963	152.261	7.806	3
6	欺骗式	2.923	19.254	6.197	45.741	0.369	1.836	1.700	77.929	2.834	0
7	欺骗式	2.955	18.086	5.188	28.159	0.374	1.162	1.201	33.858	1.461	0
8	欺骗式	2.943	17.152	10.383	58.192	1.466	2.461	3.042	177.111	6.042	1
9	欺骗式	2.908	18.162	5.710	68.533	1.621	3.953	1.193	81.919	3.910	2
10	欺骗式	2.950	22.412	6.959	2.803	2.424	4.349	9.171	25.670	0.198	3

[1]	Yingqi LU, Chengli FAN, Qiang FU, Xiaowen ZHU, Wei LI. Missile defense target threat assessment based on improved similarity measure and information entropy of IFRS [J]. Systems Engineering and Electronics, 2022, 44(4): 1230-1238.
[2]	Xinping MI, Xihong CHEN, Zan LIU, Yongjin LIU, Qiang LIU. Bispectrum feature recognition of radar signal based on entropy evaluation and modal decomposition [J]. Systems Engineering and Electronics, 2021, 43(8): 2116-2123.
[3]	Dongtao WEI, Xiaodong LIU, Peng LI, Yujin CHEN. Research on effectiveness evaluation method of equipment system based on node importance and improved information entropy [J]. Systems Engineering and Electronics, 2021, 43(12): 3614-3623.
[4]	Hongyan WANG, Jiawei JIANG, Juan PU, Yanhong WU, Da RAN. Multiple phases sectionalized modulation jamming method with low sidelobe based on cosine amplitude weighting [J]. Systems Engineering and Electronics, 2021, 43(11): 3185-3193.
[5]	Leilei ZHANG, Long XIE, Xu GAO, Tianyu SUN, Feng ZHANG. Research on synthetic reliability assessment method for high-value ammunition [J]. Systems Engineering and Electronics, 2021, 43(11): 3399-3404.
[6]	Bokai ZHANG, Weigang ZHU. Construction and key technologies of cognitive jamming decision-making system against MFR [J]. Systems Engineering and Electronics, 2020, 42(9): 1969-1975.
[7]	Henglu LI, Baixiao CHEN, Yi DING, Zhaoming ZHANG. Nearest neighbor data association algorithm based on information entropy weight [J]. Systems Engineering and Electronics, 2020, 42(4): 806-812.
[8]	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.
[9]	MAO Hu, WU Dewei, LU Hu. Parameters configuration and effectiveness analysis of multiple-tone jamming to GPS receiver [J]. Systems Engineering and Electronics, 2019, 41(8): 1699-1704.
[10]	LIANG Jialin, XIONG Wei. Capabilities assessment of the weaponry system based on combat ring [J]. Systems Engineering and Electronics, 2019, 41(8): 1810-1819.
[11]	LUO Chengkun, CHEN Yunxiang, WANG Lili, WANG Zezhou, CHANG Zheng. Effectiveness evaluation method of system-of-systems based on operation loop and improved information entropy [J]. Systems Engineering and Electronics, 2019, 41(1): 73-80.
[12]	MO Cuiqiong, BAO Zhichao, SUN Jie. Evaluation on radar seeker jamming effects based on Vague sets [J]. Systems Engineering and Electronics, 2017, 39(12): 2691-2696.
[13]	WANG Xiaowen, ZHAO Zonggui, TANG Lei. Novel quality metric for infrared and visible image fusion [J]. Journal of Systems Engineering and Electronics, 2012, 34(5): 871-875.
[14]	WU Hong, WANG Wei-ping, YANG Feng. Discretization method of continuous variables in Bayesian network parameter learning [J]. Journal of Systems Engineering and Electronics, 2012, 34(10): 2157-2162.
[15]	LI Yibing， LI Jingchao， LIN Yun. Parameter estimation of LFM signals based on fractal box dimension [J]. Journal of Systems Engineering and Electronics, 2012, 34(1): 24-27.

Parameter selection and feature representation method of jamming effect online evaluation

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 27

Related Articles 15

Recommended Articles

Metrics

Comments