智能化时代雷达导引头信号处理关键技术展望
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摘要
随着电磁及干扰技术的飞速发展,战场环境日益复杂,尤其是网络化技术以及人工智能技术的大量应用,现代战争越来越呈现出信息化、网络化、智能化特点。对雷达型空空导弹而言,智能空战时代智能组网、协同攻击将会成为雷达弹的主要作战模式,此时传统的基于个体的雷达探测、识别、抗干扰方法局限性愈加明显;协同智能化探测、智能抗干扰将逐步成为雷达导引头发展的重要方向,开发高效的反隐身、智能化抗干扰抗杂波方法将直接关系到智能时代雷达型空空导弹的成败。从目前欧美的发展趋势来看,将人工智能技术用于雷达导引技术可进一步提升雷达导引头智能化水平,有助于提升其抗干扰、抗杂波、反隐身能力。
With the development of electronic technology and anti-jamming technology,and the battlefield environment has become increasingly complex. Especially with the application of network technology and artificial intelligent technology,the informatization and networking,as well as intelligentization are shown more obviously in the modern warfare. For radar air-to-air missile,intelligent network and cooperation attack will be the main combat mode for radar missile in intelligent age. Meanwhile,the limitations of traditional individual-based radar detection,recognition and anti-jamming methods become more and more obvious. Cooperative intelligent detection and intelligent anti-jamming will gradually become important direction for the development of radar seeker. It will directly relate to success or failure of radar air-to-air missile in intelligent age to develop efficient anti-stealth and intelligent anti-jamming anti-clutter methods. From the current development trend in Europe and the United States,the application of artificial intelligence technology in radar seeker can further enhance the seeker intelligence level,and can help to improve its anti-jamming,anti-clutter and anti-stealth capabilities.
With the development of electronic technology and anti-jamming technology,and the battlefield environment has become increasingly complex. Especially with the application of network technology and artificial intelligent technology,the informatization and networking,as well as intelligentization are shown more obviously in the modern warfare. For radar air-to-air missile,intelligent network and cooperation attack will be the main combat mode for radar missile in intelligent age. Meanwhile,the limitations of traditional individual-based radar detection,recognition and anti-jamming methods become more and more obvious. Cooperative intelligent detection and intelligent anti-jamming will gradually become important direction for the development of radar seeker. It will directly relate to success or failure of radar air-to-air missile in intelligent age to develop efficient anti-stealth and intelligent anti-jamming anti-clutter methods. From the current development trend in Europe and the United States,the application of artificial intelligence technology in radar seeker can further enhance the seeker intelligence level,and can help to improve its anti-jamming,anti-clutter and anti-stealth capabilities.
引文
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[26]Li Feijiang,Qian Yuhua,Wang Jieting,et al.Multigranulation Information Fusion:A Dempster-Shafer Evidence Theory Based Clustering Ensemble Method[C]∥International Conference on Machine Learning&Cybernetics,Guangzhou,2015.
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[28]Wang Guohong,Li Lin,Yu Hongbo.A Modified Hough Transform TBD Algorithm Based on Point Set Merging[J].Acta Aeronautica et Astronautica Sinica,2017,38(1):1-11.
[29]Bai Jianxiong,Wang Jun.Weak Target Detection Using Dynamic Programming TBD in CDMA Based Passive Radar[C]∥IET International Radar Conference,Guilin,2009.
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[33]Fan Meimei,Liao Dongping,Ding Xiaofeng,et al.Waveform Design for Target Recognition on the Background of Clutter[C]∥8th European Radar Conference,Manchester,2011.
[34]Wei Yimin,Meng Huadong,Wang Xiqin.Adaptive Single-Tone Waveform Design for Target Recognition in Cognitive Radar[C]∥IET International Radar Conference,Guilin,2009.
[35]Yu Fuping,Feng Youqian,Gao Dahua,et al.Study of Anti Jamming of Radar Signal Based on Sparse Decomposition[C]∥International Symposium on Intelligence Information Processing&Trusted Computing,Huanggang,2010.
[2]Zhang Shunsheng,Zeng Tao,Long Teng,et al.Dim Target Detection Based on Keystone Transform[C]∥IEEEInternational Radar Conference,Arlington,2005:889-894.
[3]Rao Xuan,Tao Haihong,Su Jia,et al.Axis Rotation MTD Algorithm for Weak Target Detection[J].Digital Signal Process,2014,26(1):81-86.
[4]Wei Yinsheng,Liu Yongtan.New Anti-Jamming Waveform Designing and Processing for HF Radar[C]∥Proceedings of CIE International Conference on Radar,2001:281-284.
[5]周志华.机器学习[M].北京:清华大学出版社,2016.Zhou Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016.(in Chinese)
[6]Hurtado M,Nehorai A.Polarization Diversity for Detecting Targets in Inhomogeneous Clutter[C]∥International Waveform Diversity and Design Conference,Pisa,2007:383-386.
[7]Bundy A.Preparing for Future of Artificial Intelligence[J].AI&Socity,2017,32(2):285-287.
[8]DARPA.The Target Recognition and Adaption in Contested Environments(TRACE)Program Homepage[EB/OL].[2018-12-24].https:∥www.technion.ac.il/~research/DARPA-BAA-15-09.pdf.
[9]王雪松.雷达极化技术研究现状及展望[J].雷达学报,2016,5(2):119-131.Wang Xuesong.Status and Prospects of Radar Polarimetry Techniques[J].Journal of Radars,2016,5(2):119-131.(in Chinese)
[10]Missile Defense Agency.Multiple Kill Vehicle Completes Hover Test[EB/OL].(2008-12-03)[2018-12-23].https:∥www.mda.millglobal/documents/pdf/08fyiollo.pdf.
[11]叶继坤,雷虎民,薛东风,等.基于几何关系的多导弹协同跟踪算法[J].系统工程理论与实践,2012,32(10):2332-2338.Ye Jikun,Lei Humin,Xue Dongfeng,et al.Multiple Missile Cooperative Tracking Algorithm Base on Geometric Relation[J].Systems Engineering Theory&Practice,2012,32(10):2332-2338.(in Chinese)
[12]谢宇婷,周军,卢晓东.基于多导弹协同目标探测的空间配准算法[J].航空兵器,2018(2):9-15.Xie Yuting,Zhou Jun,Lu Xiaodong.Spatial Registration Algorithm Based on Multi-Missile Cooperative Target Detection[J].Aero Weaponry,2018(2):9-15.(in Chinese)
[13]李云坤,陈伟,曹旭东.粒子滤波检测前跟踪算法的粒子比优化方法研究[J].航空兵器,2017(5):25-30.Li Yunkun,Chen Wei,Cao Xudong.Particle Ratio Optimization of Particle Filter Track-before-Detect Alogrithm[J].Aero Weaponry,2017(5):25-30.(in Chinese)
[14]胡仕友,赵英海.导弹武器智能精确制导技术发展分析[J].战术导弹技术,2017(2):1-6.Hu Shiyou,Zhao Yinghai.Analysis on the Development of Intelligent Precision Guidance Technology for Missile Weapons[J].Tactical Missile Technology,2017(2):1-6.(in Chinese)
[15]Carretero-Moya J,Gismero-Menoyo J,Blanco-del-Campo A,et al.Statistical Analysis of a High-Resolution SeaClutter Database[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(4):2024-2037.
[16]Le Cun Y,Bengio Y,Hinton G.Deep Learning[J].Nature,2015,521(7553):436-444.
[17]Russell S J,Norvig P.Artificial Intelligence:A Modern Approach[M].Prentice-Hall,Inc.Upper Saddle River,NJ,USA,1995.
[18]曲成华,史新虎,张程,等.基于人工智能的机动目标跟踪处理研究[J].科技视界,2018(13):3-5.Qu Chenghua,Shi Xinhu,Zhang Cheng,et al.Research on Maneuvering Target Tracking Processing Based on Artificial Intelligence[J].Science&Technology View,2018(13):3-5.(in Chinese)
[19]Del-Rey-Maestre N,Mata-Moya D,Jarabo-Amores M P,et al.Artificial Intelligence Techniques for Small Boats Detection in Radar Clutter.Real Datavalidation[J].Engineering Applications of Artificial Intelligence,2018,67:296-308.
[20]王义宁,姜玉宪.空战对策的发展现状及未来[J].电光与控制,2000(2):13-17.Wang Yining,Jiang Yuxian.The Current Development Situation and Future of the Air Combat Game[J].Electronics Optics&Control,2000(2):13-17.(in Chinese)
[21]Carretero-Moya J,De Maio A,Gismero-Menoyo J,et al.Experimental Performance Analysis of Distributed Target Coherent Radar Detectors[J].IEEE Transactions on Aerospace and Electronic Systems,2012,48(3):2216-2238.
[22]陈小龙,关键,何友,等.高分辨稀疏表示及其在雷达动目标检测中的应用[J].雷达学报,2017,6(3):239-251.Chen Xiaolong,Guan Jian,He You,et al.High-Resolution Sparse Representation and Its Applications in Radar Moving Target Detection[J].Journal of Radars,2017,6(3):239-251.(in Chinese)
[23]Xu J,Liao G,So H C.Space-Time Adaptive Processing with Vertical Frequency Diverse Array for Range-Ambiguous Clutter Suppression[J].IEEE Transactions on Geoscience&Remote Sensing,2016,54(9):5352-5364.
[24]Zhang Xing,Yao Di,Yang Qiang,et al.KnowledgeBased Generalized Side-Lobe Canceller for Ionospheric Clutter Suppression in HFSWR[J].Remote Sensing,2018,10(1):104.
[25]Li T,Corchado J M,Bajo J,et al.Effectiveness of Bayesian Filters:An Information Fusion Perspective[J].Information Sciences,2016,329:670-689.
[26]Li Feijiang,Qian Yuhua,Wang Jieting,et al.Multigranulation Information Fusion:A Dempster-Shafer Evidence Theory Based Clustering Ensemble Method[C]∥International Conference on Machine Learning&Cybernetics,Guangzhou,2015.
[27]Piras L,Giacinto G.Information Fusion in Content Based Image Retrieval:A Comprehensive Overview[J].Information Fusion,2017,37:50-60.
[28]Wang Guohong,Li Lin,Yu Hongbo.A Modified Hough Transform TBD Algorithm Based on Point Set Merging[J].Acta Aeronautica et Astronautica Sinica,2017,38(1):1-11.
[29]Bai Jianxiong,Wang Jun.Weak Target Detection Using Dynamic Programming TBD in CDMA Based Passive Radar[C]∥IET International Radar Conference,Guilin,2009.
[30]Mishra A K,Baker C.A Cognitive Diversity Framework for Radar Target Classification[EB/OL].(2011-11-30)[2018-12-23].http:∥arxiv.org/pdf/1110.6589.pdf.
[31]庞存锁,刘磊,单涛.基于短时分数阶傅里叶变换的时频分析方法[J].电子学报,2014,42(2):347-352.Pang Cunsuo,Liu Lei,Shan Tao.Time-Frequency Analysis Method Based on Short-Time Fractional Fourier Transform[J].Acta Electronica Sinica,2014,42(2):347-352.(in Chinese)
[32]马君国,赵宏钟,张军,等.基于高阶统计量的低分辨雷达空间目标识别算法[J].信号处理,2006,22(2):211-214.Ma Junguo,Zhao Hongzhong,Zhang Jun,et al.Space Target Recognition Algorithm of Low Resolution Radar Based on High Order Statistics[J].Signal Processing,2006,22(2):211-214.(in Chinese)
[33]Fan Meimei,Liao Dongping,Ding Xiaofeng,et al.Waveform Design for Target Recognition on the Background of Clutter[C]∥8th European Radar Conference,Manchester,2011.
[34]Wei Yimin,Meng Huadong,Wang Xiqin.Adaptive Single-Tone Waveform Design for Target Recognition in Cognitive Radar[C]∥IET International Radar Conference,Guilin,2009.
[35]Yu Fuping,Feng Youqian,Gao Dahua,et al.Study of Anti Jamming of Radar Signal Based on Sparse Decomposition[C]∥International Symposium on Intelligence Information Processing&Trusted Computing,Huanggang,2010.