雷达欺骗式干扰检测与实现
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摘要
如今高概率的雷达有源欺骗式干扰信号检测是雷达抗干扰过程的基础,但是目前我军的雷达有源干扰信号的检测和识别在很大程度上还依赖于雷达操作员的经验,在复杂的电子战环境中,要做到实时正确地检测是否存在干扰对雷达操作员难以实现。因此,研究有源干扰的智能监测和识别,不仅可以消除人为因素的不利影响,还可以提高识别率,增强雷达的对抗能力,具有极其重要的军事意义。基于以上考虑,本文首先在已有工作的基础上,创新地提出了基于免疫遗传算法的神经网络分类器,实现了对雷达有源干扰信号的自动高精度的分类和识别;同时,首次提出了一种针对脉冲多普勒雷达欺骗式干扰的硬件可实现实时检测系统,以应用于实时性能要求较高的电子攻防战中;最后,本文设计了该实时检测系统的硬件,软件模块,并完成了相关的系统设计工作。
本文的主要工作可以概括如下:
1)本文创新地提出了基于免疫遗传算法的神经网络分类器,实现对雷达有源干扰信号的自动分类识别算法:首先从信号处理的角度出发,研究分析了雷达有源干扰信号在时域、频域和其它变换域上的特征,提取了多个特征参数,从不同角度采用不同方法研究了雷达有源干扰信号的特征。之后结合本文提出的基于免疫系统的径向基函数神经网络用于实现对信号特征的分类以判断是否存在欺骗式雷达干扰信号。仿真验证和性能分析后并通过比较发现,该免疫神经网络分类器精度性能优于目前已有的基于神经网络的其他分类器性能,同时结合免疫算法,又具有较好的时间复杂度。
2)本文针对目前雷达欺骗干扰信号检测算法实时性能普遍不高的情况,提出了一种针对脉冲多普勒雷达的欺骗式干扰的硬件可实现的实时检测方法:针对目前常见的脉冲多普勒雷达和常见的DRFM体制的欺骗干扰信号,研究了相应的信号特征并提出了一种可以硬件实现的实时欺骗式干扰检测方法。仿真表明该方法能够有效的检测出针对脉冲多普勒雷达的距离项、速度、加速度上的欺骗式干扰信号。
3)本文在提出实时处理算法的基础上,进行了硬件软件设计并完成了最终的硬件系统设计:针对提出的算法,结合现有的硬件平台和手段考虑,选择相应的硬件配置并设计出了相应的硬件平台,就其中的主要关键问题进行了实现详细的硬件设计和软件设计,实现了相应的硬件平台并进行了测试。
Active deceive jamming plays a crucial role in modern electronic warfare. The objective of detection and identification is to decide the type of active deceive jamming signal without any priori knowledge about the enemy, and then take the corresponding measures to suppress the jamming. The detection and identification of active deceive jamming is studied in this dissertation. First, an improved radar active deceive jamming signal detection algorithm is proposed, then a real-time radar active deceive jamming signal detection method is proposed to detect DRFM deceive jamming signal, At last, The hardware system is implemented. The main work of this dissertation is summarized as follows:
1. A neural network classifier based on immune system is proposed for radar active deceive jamming signal detection after signal characters extraction: First different feature extraction methods are used to explore the characteristics of active deceive jamming signal. Then RBFNN based on immune system is studied to recognize the active deceive jamming automatically. The validities and superiorities of two classifiers are shown in the experiments, and several conclusions are obtained: The RBFNN can perform the identification of active jamming signal effectively also it is simple to implement and has a good performance.
2. Based on the study of Pulse Dropper radar and DRFM active deceive jamming signal, a engineering method is proposed to detect DRFM active deceive jamming towards Pulse Dropper radar in this dissertation. Simulation results show that the method can effectively detect DRFM active deceive jamming signal.
3. According to the DRFM active deceive jamming detection method and current hardware implement technology, the radar active deceive jamming detection system was implemented. Hardware system based on ADC and FPGA was designed and software of the system including spectrum estimation was also designed. Experiments show that the hardware system can effectively detect DRFM active deceive jamming signal.
本文的主要工作可以概括如下:
1)本文创新地提出了基于免疫遗传算法的神经网络分类器,实现对雷达有源干扰信号的自动分类识别算法:首先从信号处理的角度出发,研究分析了雷达有源干扰信号在时域、频域和其它变换域上的特征,提取了多个特征参数,从不同角度采用不同方法研究了雷达有源干扰信号的特征。之后结合本文提出的基于免疫系统的径向基函数神经网络用于实现对信号特征的分类以判断是否存在欺骗式雷达干扰信号。仿真验证和性能分析后并通过比较发现,该免疫神经网络分类器精度性能优于目前已有的基于神经网络的其他分类器性能,同时结合免疫算法,又具有较好的时间复杂度。
2)本文针对目前雷达欺骗干扰信号检测算法实时性能普遍不高的情况,提出了一种针对脉冲多普勒雷达的欺骗式干扰的硬件可实现的实时检测方法:针对目前常见的脉冲多普勒雷达和常见的DRFM体制的欺骗干扰信号,研究了相应的信号特征并提出了一种可以硬件实现的实时欺骗式干扰检测方法。仿真表明该方法能够有效的检测出针对脉冲多普勒雷达的距离项、速度、加速度上的欺骗式干扰信号。
3)本文在提出实时处理算法的基础上,进行了硬件软件设计并完成了最终的硬件系统设计:针对提出的算法,结合现有的硬件平台和手段考虑,选择相应的硬件配置并设计出了相应的硬件平台,就其中的主要关键问题进行了实现详细的硬件设计和软件设计,实现了相应的硬件平台并进行了测试。
Active deceive jamming plays a crucial role in modern electronic warfare. The objective of detection and identification is to decide the type of active deceive jamming signal without any priori knowledge about the enemy, and then take the corresponding measures to suppress the jamming. The detection and identification of active deceive jamming is studied in this dissertation. First, an improved radar active deceive jamming signal detection algorithm is proposed, then a real-time radar active deceive jamming signal detection method is proposed to detect DRFM deceive jamming signal, At last, The hardware system is implemented. The main work of this dissertation is summarized as follows:
1. A neural network classifier based on immune system is proposed for radar active deceive jamming signal detection after signal characters extraction: First different feature extraction methods are used to explore the characteristics of active deceive jamming signal. Then RBFNN based on immune system is studied to recognize the active deceive jamming automatically. The validities and superiorities of two classifiers are shown in the experiments, and several conclusions are obtained: The RBFNN can perform the identification of active jamming signal effectively also it is simple to implement and has a good performance.
2. Based on the study of Pulse Dropper radar and DRFM active deceive jamming signal, a engineering method is proposed to detect DRFM active deceive jamming towards Pulse Dropper radar in this dissertation. Simulation results show that the method can effectively detect DRFM active deceive jamming signal.
3. According to the DRFM active deceive jamming detection method and current hardware implement technology, the radar active deceive jamming detection system was implemented. Hardware system based on ADC and FPGA was designed and software of the system including spectrum estimation was also designed. Experiments show that the hardware system can effectively detect DRFM active deceive jamming signal.
引文
[1] August Golden Jr., Radar Electronic Warfare, AIAA, 1988
[2] Alfred Price, The History of US Electronic Warfare, Assn of Old Crows, 1984
[3] Edward Waltz,Information warfare principles and operations,Artech House, 1998
[4]梁百川,信息战与信息国防,电子对抗,1996,(4):43-47
[5] Merrill I. Skolnik, Radar Handbook, Third Edition, McGraw-Hill Professional, 2008
[6] D. Curtis Schleher, Electronic warfare in the information age, Artech House, 1999
[7]黄鹤,陈怀新,基于参量直方分布的数字信号调制识别,电讯技术,2003,(2):51-54
[8]戴威,王有政,王京,基于AR模型的调制盲识别方法,电子学报,2001,(2):1890-1892
[9] A. Swami, M. Brian, Sadler. Hierarchical. Digital modulation classification using cumulants. IEEE Trans. on communications, 2000, 48(3): 416-429
[10] A. William, W. A. Gardner, C. Brown. Spectral correlation of modulated signals: Part II-digital modulation. IEEE Trans. on communications, 1987, 35(6): 595-601
[11] H. Liang, Ho. K .C. Identification of digital modulation types using the wavelet transform. IEEE Military communications conference Proceedings, 1999, 1(31): 427-431
[12] P. K. Simpson. Fuzzy min-max neural networks part 2: clustering. IEEE Fuzzy Systems, 1993, 1(1): 32-45
[13] J. Kittler, M. Hatef, R. P. W. Dui, et al. On combining classifiers. IEEE Trans. on Pattern Analysis and Machine intelligence, 1998, 20(3): 226-239
[14] M.Greco, F.Gini et al. Effect of phase and range gate pull-off delay quantization on jammed signal. IEE Proc. Radar Sonar and Navigation, 2006, 153(5), 454-459
[15]张葛祥,雷达辐射源信号智能识别方法研究,西南交通大学,2005
[16] Ling Lu, Jie Yang, Hong Wang, et al. The analysis and feature extraction of non-stationary random characteristics of radar jammed signal. IEEE the 3rd international symposium on Electromagnetic Compatibility,2002:660-663
[17] S.Haykin, T.K.Bhattacharya. Modular Learning Strategy for Signal Detection in aNon-stationary Environment. IEEE Trans. on SP,1997,6(45):1619-1637
[18]王党卫,秦江敏,马晓岩,基于模式分类检测的射频噪声干扰抑制方法,空军雷达学院学报,2003,17(1):4-6
[19] C. J. Oliver, R. G. White, Radar clutter classification based on noise models and neural networks, Proc. IEEE. RADAR-90, Washington ,1990, 329-334
[20]贾鑫,卢昱,一种实验型雷达干扰信号自动识别系统,现代雷达,1995,(2):16-21
[21]吕强,李建勋,秦江敏等,基于神经网络的雷达抗转发式距离欺骗干扰方法,系统工程与电子技术,2005,27(2):240-243
[22] Haykin S. et al, Signal detection in a non-stationary environment reformulated as an adaptive pattern classification problem, Proceedings of the IEEE, 86(1), 1998: 2325-2344.
[23]李建勋,秦江敏,马晓岩,Kohonen网络用于雷达抗速度欺骗干扰中的特征提取,雷达科学与技术,2004,2(2):82-86
[24]赵雪飞,PD雷达抗距离速度同步拖引干扰的频谱识别法,雷达与对抗,2005,(3):21-24
[25]张建军,刘泉,基于小波分析的距离拖引干扰检测,武汉理工大学学报,2006,28(1):99-101
[26] Richard J. Wiegand, Radar Electronic Countermeasures System Design, Artech House, 1991
[27] G. Richard Curry, Radar System Performance Modeling, Second Edition, Artech House, 2004
[28]郁文贤,舰船雷达目标识别的智能化方法研究,国防科技大学,1992.
[29]王伟,张汉华等,低分辨雷达的目标特征提取方法研究,国防科技大学学报2003,24(2):31-35
[30] Hennry Leung, Jiangfeng Wu, Bayesian and Dempster Shafe target identification for radar surveillance, IEEE Trans On A.E.S, 36(2), 2000.
[31] Moody J, Darken C. Fast learning in networks of locally-tuned processing units. Neural Computation, 1989, (1): 281-294.
[32] Light. W. A., "Some aspects of radial basis function approximate" in Approximation Theory, Spline Function and Applications, S. P. Singh, ed., NATO ASI vol.256, Boston: Kluwer Academic Publishers, pp. 163-190.
[33] C. A. Janeway Jr., P. Travers. Immunobiology the immune system in health and disease[M]. New York: Garland Publishing Inc, 1994.
[34] Smith, D. J., Forrest, S., Perelson, A. S. Immunological memory is associative. Immunity Based Systems, Int. Conf. on Multiagent Systems, Kyoto: Workshop Notes, Workshop 4, 1996. 62–70.
[35]臧小刚,宫新保,常成,凌小峰,唐斌,一种基于免疫系统的RBF网络在线训练方法,电子学报,2008,(7):1396-1400
[36] Martin Streetly, Jane's Radar and Electronic Warfare Systems 2008-2009, Jane's Information Group, 2008
[37] Merrill I. Skolnik, Introduction to Radar Systems, McGraw-Hill International Editions: Electrical Engineering Series, 2000
[38] Alan V. Oppenheim, Ronald Schafer, Wayne Padgett, Mark T Yoder, Discrete-Time Signal Processing, Prentice Hall, 1998
[39] Roome S J. Digital radio frequency memory. Electronics & Communication Engineering Journal, 1990,(8):147-153
[40] Berger S D, Digital radio frequency memory linear range gate stealer spectrum. IEEE Transactions on Aerospace and Electronic Systems,2003,39 (2):725-735
[41] Analog Device, AD9230 Datasheet, 2007
[42] Eric Bogatin, Signal Integrity– Simplified, Prentice Hall PTR, 2003
[43] Howard Johnson, Martin Graham, High Speed Digital Design: A Handbook of Black Magic, Prentice Hall PTR, 1993
[44] Altera, Altera Stratix II Device Datasheet, 2008
[45] Uwe Meyer-Baese, Digital Signal Processing with Field Programmable Gate Arrays, Springer, 2004
[46] Altera, Quartus II handbooks, 2008
[47] Steve Kilts, Advanced FPGA Design-Architecture, Implementation, and Optimization, John Wiley & Sons, 2007
[48] Mentor Graphics, HyperLynx user guide, 2007
[49]中兴通讯康讯EDA设计部,EDA工具手册(内参),2006
[2] Alfred Price, The History of US Electronic Warfare, Assn of Old Crows, 1984
[3] Edward Waltz,Information warfare principles and operations,Artech House, 1998
[4]梁百川,信息战与信息国防,电子对抗,1996,(4):43-47
[5] Merrill I. Skolnik, Radar Handbook, Third Edition, McGraw-Hill Professional, 2008
[6] D. Curtis Schleher, Electronic warfare in the information age, Artech House, 1999
[7]黄鹤,陈怀新,基于参量直方分布的数字信号调制识别,电讯技术,2003,(2):51-54
[8]戴威,王有政,王京,基于AR模型的调制盲识别方法,电子学报,2001,(2):1890-1892
[9] A. Swami, M. Brian, Sadler. Hierarchical. Digital modulation classification using cumulants. IEEE Trans. on communications, 2000, 48(3): 416-429
[10] A. William, W. A. Gardner, C. Brown. Spectral correlation of modulated signals: Part II-digital modulation. IEEE Trans. on communications, 1987, 35(6): 595-601
[11] H. Liang, Ho. K .C. Identification of digital modulation types using the wavelet transform. IEEE Military communications conference Proceedings, 1999, 1(31): 427-431
[12] P. K. Simpson. Fuzzy min-max neural networks part 2: clustering. IEEE Fuzzy Systems, 1993, 1(1): 32-45
[13] J. Kittler, M. Hatef, R. P. W. Dui, et al. On combining classifiers. IEEE Trans. on Pattern Analysis and Machine intelligence, 1998, 20(3): 226-239
[14] M.Greco, F.Gini et al. Effect of phase and range gate pull-off delay quantization on jammed signal. IEE Proc. Radar Sonar and Navigation, 2006, 153(5), 454-459
[15]张葛祥,雷达辐射源信号智能识别方法研究,西南交通大学,2005
[16] Ling Lu, Jie Yang, Hong Wang, et al. The analysis and feature extraction of non-stationary random characteristics of radar jammed signal. IEEE the 3rd international symposium on Electromagnetic Compatibility,2002:660-663
[17] S.Haykin, T.K.Bhattacharya. Modular Learning Strategy for Signal Detection in aNon-stationary Environment. IEEE Trans. on SP,1997,6(45):1619-1637
[18]王党卫,秦江敏,马晓岩,基于模式分类检测的射频噪声干扰抑制方法,空军雷达学院学报,2003,17(1):4-6
[19] C. J. Oliver, R. G. White, Radar clutter classification based on noise models and neural networks, Proc. IEEE. RADAR-90, Washington ,1990, 329-334
[20]贾鑫,卢昱,一种实验型雷达干扰信号自动识别系统,现代雷达,1995,(2):16-21
[21]吕强,李建勋,秦江敏等,基于神经网络的雷达抗转发式距离欺骗干扰方法,系统工程与电子技术,2005,27(2):240-243
[22] Haykin S. et al, Signal detection in a non-stationary environment reformulated as an adaptive pattern classification problem, Proceedings of the IEEE, 86(1), 1998: 2325-2344.
[23]李建勋,秦江敏,马晓岩,Kohonen网络用于雷达抗速度欺骗干扰中的特征提取,雷达科学与技术,2004,2(2):82-86
[24]赵雪飞,PD雷达抗距离速度同步拖引干扰的频谱识别法,雷达与对抗,2005,(3):21-24
[25]张建军,刘泉,基于小波分析的距离拖引干扰检测,武汉理工大学学报,2006,28(1):99-101
[26] Richard J. Wiegand, Radar Electronic Countermeasures System Design, Artech House, 1991
[27] G. Richard Curry, Radar System Performance Modeling, Second Edition, Artech House, 2004
[28]郁文贤,舰船雷达目标识别的智能化方法研究,国防科技大学,1992.
[29]王伟,张汉华等,低分辨雷达的目标特征提取方法研究,国防科技大学学报2003,24(2):31-35
[30] Hennry Leung, Jiangfeng Wu, Bayesian and Dempster Shafe target identification for radar surveillance, IEEE Trans On A.E.S, 36(2), 2000.
[31] Moody J, Darken C. Fast learning in networks of locally-tuned processing units. Neural Computation, 1989, (1): 281-294.
[32] Light. W. A., "Some aspects of radial basis function approximate" in Approximation Theory, Spline Function and Applications, S. P. Singh, ed., NATO ASI vol.256, Boston: Kluwer Academic Publishers, pp. 163-190.
[33] C. A. Janeway Jr., P. Travers. Immunobiology the immune system in health and disease[M]. New York: Garland Publishing Inc, 1994.
[34] Smith, D. J., Forrest, S., Perelson, A. S. Immunological memory is associative. Immunity Based Systems, Int. Conf. on Multiagent Systems, Kyoto: Workshop Notes, Workshop 4, 1996. 62–70.
[35]臧小刚,宫新保,常成,凌小峰,唐斌,一种基于免疫系统的RBF网络在线训练方法,电子学报,2008,(7):1396-1400
[36] Martin Streetly, Jane's Radar and Electronic Warfare Systems 2008-2009, Jane's Information Group, 2008
[37] Merrill I. Skolnik, Introduction to Radar Systems, McGraw-Hill International Editions: Electrical Engineering Series, 2000
[38] Alan V. Oppenheim, Ronald Schafer, Wayne Padgett, Mark T Yoder, Discrete-Time Signal Processing, Prentice Hall, 1998
[39] Roome S J. Digital radio frequency memory. Electronics & Communication Engineering Journal, 1990,(8):147-153
[40] Berger S D, Digital radio frequency memory linear range gate stealer spectrum. IEEE Transactions on Aerospace and Electronic Systems,2003,39 (2):725-735
[41] Analog Device, AD9230 Datasheet, 2007
[42] Eric Bogatin, Signal Integrity– Simplified, Prentice Hall PTR, 2003
[43] Howard Johnson, Martin Graham, High Speed Digital Design: A Handbook of Black Magic, Prentice Hall PTR, 1993
[44] Altera, Altera Stratix II Device Datasheet, 2008
[45] Uwe Meyer-Baese, Digital Signal Processing with Field Programmable Gate Arrays, Springer, 2004
[46] Altera, Quartus II handbooks, 2008
[47] Steve Kilts, Advanced FPGA Design-Architecture, Implementation, and Optimization, John Wiley & Sons, 2007
[48] Mentor Graphics, HyperLynx user guide, 2007
[49]中兴通讯康讯EDA设计部,EDA工具手册(内参),2006