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

摘要/Abstract

摘要：

针对利用雷达侦察信号进行干扰效果在线评估时参数冗余、信号变化程度判别不清的问题,提出雷达干扰效果在线评估参数筛选与特征表示方法。对多功能海上雷达信号,利用信息熵筛选剧烈变化参数,通过盒维数和皮尔逊相关系数提取信号变化特征,结合支持向量机实现对未知威胁的干扰效果在线评估。仿真实验表明,当信号参数偏离误差小于10%时,新方法的评估准确率高于92%,显著提高了雷达干扰效果在线评估的可靠性。

关键词: 干扰效果, 在线评估, 信息熵, 盒维数, 皮尔逊相关系数

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

中图分类号:

TN974

雷震烁, 刘松涛, 葛杨, 温镇铭. 干扰效果在线评估参数筛选与特征表示方法[J]. 系统工程与电子技术, 2020, 42(12): 2755-2760.

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.

图/表 6

图1

表1

表2

图2

图3

图4

参考文献 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]	卢盈齐, 范成礼, 付强, 朱晓雯, 李威. 基于改进IFRS相似度和信息熵的反导作战目标威胁评估[J]. 系统工程与电子技术, 2022, 44(4): 1230-1238.
[2]	米新平, 陈西宏, 刘赞, 刘永进, 刘强. 基于熵评价模态分解的雷达信号双谱特征识别[J]. 系统工程与电子技术, 2021, 43(8): 2116-2123.
[3]	魏东涛, 刘晓东, 李鹏, 陈玉金. 基于节点重要度与改进信息熵的装备体系效能评估方法研究[J]. 系统工程与电子技术, 2021, 43(12): 3614-3623.
[4]	王宏艳, 降佳伟, 蒲娟, 吴彦鸿, 冉达. 基于余弦幅度加权的低旁瓣多相位分段调制干扰方法[J]. 系统工程与电子技术, 2021, 43(11): 3185-3193.
[5]	张雷雷, 解龙, 高旭, 孙天宇, 张峰. 高价值弹药可靠性综合评定方法研究[J]. 系统工程与电子技术, 2021, 43(11): 3399-3404.
[6]	李恒璐, 陈伯孝, 丁一, 张钊铭. 基于信息熵权的最近邻域数据关联算法[J]. 系统工程与电子技术, 2020, 42(4): 806-812.
[7]	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.
[8]	梁家林, 熊伟. 基于作战环的武器装备体系能力评估方法[J]. 系统工程与电子技术, 2019, 41(8): 1810-1819.
[9]	罗承昆, 陈云翔, 王莉莉, 王泽洲, 常政. 基于作战环和改进信息熵的体系效能评估方法[J]. 系统工程与电子技术, 2019, 41(1): 73-80.
[10]	莫翠琼, 鲍志超, 孙杰. 基于Vague集的雷达导引头干扰效果评估[J]. 系统工程与电子技术, 2017, 39(12): 2691-2696.
[11]	张薇玮, 丁文锐, 刘春辉. 复杂环境中无人机数据链干扰效果预测方法[J]. 系统工程与电子技术, 2016, 38(4): 760-766.
[12]	郭明，王书满. 基于区域和方向方差加权信息熵的图像融合[J]. Journal of Systems Engineering and Electronics, 2013, 35(4): 720-724.
[13]	祁晓明，魏瑞轩，沈　东，王树磊. 面向目标不确定的多无人机鲁棒协同搜索[J]. 系统工程与电子技术, 2013, 35(11): 2303-2308.
[14]	王晓文，赵宗贵，汤磊. 一种新的红外与可见光图像融合评价方法[J]. Journal of Systems Engineering and Electronics, 2012, 34(5): 871-875.
[15]	吴红, 王维平, 杨峰. 贝叶斯网络参数学习中的连续变量离散化方法[J]. Journal of Systems Engineering and Electronics, 2012, 34(10): 2157-2162.