跳频码序列建模与预测研究
|
摘要
随着通信领域的频率竞争愈演愈烈,惯用的定频通信受到了严重威胁。为了保证己方正常可靠的通信,一种抗干扰通信体制——跳频通信系统应运而生。跳频通信技术具有优良的抗干扰性能和多址组网性能,不但在军事通信中得到了广泛的应用,如美国的SINCGARS系列超短波跳频电台和联合战术信息分发系统JTIDS,而且与我们的日常生活密切相关,在民用移动通信中也得到了广泛应用,如GSM、HomeRF(家庭射频)、Bluetooth(蓝牙)中都应用了跳频技术。
跳频通信通过躲避干扰的方法来达到优良的抗干扰性能,仅当干扰信号频率与跳频信号频率相同时,才能形成干扰,因此对其干扰和抗干扰的研究成为通信电子战的一个重要部分。而用来控制载波频率跳变的序列对跳频通信系统的抗干扰能力有着决定性的影响,所以对跳频序列的研究成为当前国内外的一个热点。
本文主要研究跳频码序列的建模与预测,即对跳频序列的历史观察值x1 , x2 , , xn ?1 ,xn进行建模,预测未来时刻xn + k( k> 0)的值。具体工作内容如下:
1)阐述了常规跳频通信系统的原理,对系统各个组成部分的作用作了详细介绍,综述了跳频组网和跳频同步方法,并对部分常见跳频序列的设计进行了研究,最后对常规跳频通信系统进行了仿真实验。
2)对基于移位寄存器产生的跳频码进行了分析,总结出此类跳频码的构造规律以达到预测的目的。本文提出了一种专门针对基于移位寄存器构造的跳频序列的预测方法,在已知很少频点时即可进行预测,预测速度快,有效预测率高。
3)运用非线性预测方法对常见跳频码进行预测。文中主要运用BP神经网络、RBF神经网络、Bernstein多项式、Volterra自适应滤波器四种预测模型进行预测,通过理论分析和仿真实验对各种预测模型的性能进行了比较分析,并通过半实物仿真实验验证了各种预测算法的有效性。
4)对混沌时间序列进行了多步和多模式预测。本文研究了两种多步预测方法,首先从理论上分析了两种方法的优劣,然后再用上述四种非线性预测模型对两种方法进行了仿真实验,并在以上非线性预测模型的基础上进行了多模式预测,仿真结果表明多模式预测能提高序列的有效预测率。
As the competition in frequency become more and more intensively in communication domain, the communication with fixed frequency suffers severe threats. In order to guarantee our communication, a new anti-Jamming communication system emerges, namely Frequency Hopping Communication System(FHCS). Duing to its outstanding counter-interference capability and Multiple Access properties, FHCS not only becomes popular in military communication, such as SINCGARS and JTIDS, but also is widely used in the civil mobile communications, including GSM, HomeRF and Bluetooth.
FH communication has anti-scout and anti-jamming capability by escaping the jamming. It can work when the frequency hops irregularly and quickly, which can’t be intercepted, recognised and interfered easily. It is interfered only when the signal frequency equals FH signal frequency. So the researches on anti-jamming and jamming of FHCS are a very important part of electronic warfare.The FH sequences which are used to control the frequency affects the anti-jamming capability crucially.
In order to improve the anti-jamming and jamming capability of FHCS, the main work is to predict FH Sequence in the dissertation, including the following several aspects.
1) The basic principle of FHCS’s and FH sequences is presented, the components of the system are described detailedly, FH mutiple access and synchronization methods are summarized, the design for FH sequences is studied. At last, computer simulations are done.
2) The construction principle of some familiar FH Sequences is analyzed. A novel predicting method aiming at the FH Sequences producted by shift-register is proposed. Computer simulations show the feasibility and efficiency of the method.
3) Several nonlinear predicting methods are analyzed and studied. BP Neural Network, RBF Neural Network, Bernstein polynomial, Volterra adaptive filter are used for predicting. The advantages and disadvantages of each predicting method in predicting different kinds of sequences are analyzed.
4) Multi-step and multi-mode forecasting for chaotic time series are researched. Compare the performance of two multi-step forecasting methods according to theory analysis and experimental results. Multi-mode forecasting based on the above four nonlinear predicting methods is made, and the simulation results indicate that effective forecasting probability can be increased.
跳频通信通过躲避干扰的方法来达到优良的抗干扰性能,仅当干扰信号频率与跳频信号频率相同时,才能形成干扰,因此对其干扰和抗干扰的研究成为通信电子战的一个重要部分。而用来控制载波频率跳变的序列对跳频通信系统的抗干扰能力有着决定性的影响,所以对跳频序列的研究成为当前国内外的一个热点。
本文主要研究跳频码序列的建模与预测,即对跳频序列的历史观察值x1 , x2 , , xn ?1 ,xn进行建模,预测未来时刻xn + k( k> 0)的值。具体工作内容如下:
1)阐述了常规跳频通信系统的原理,对系统各个组成部分的作用作了详细介绍,综述了跳频组网和跳频同步方法,并对部分常见跳频序列的设计进行了研究,最后对常规跳频通信系统进行了仿真实验。
2)对基于移位寄存器产生的跳频码进行了分析,总结出此类跳频码的构造规律以达到预测的目的。本文提出了一种专门针对基于移位寄存器构造的跳频序列的预测方法,在已知很少频点时即可进行预测,预测速度快,有效预测率高。
3)运用非线性预测方法对常见跳频码进行预测。文中主要运用BP神经网络、RBF神经网络、Bernstein多项式、Volterra自适应滤波器四种预测模型进行预测,通过理论分析和仿真实验对各种预测模型的性能进行了比较分析,并通过半实物仿真实验验证了各种预测算法的有效性。
4)对混沌时间序列进行了多步和多模式预测。本文研究了两种多步预测方法,首先从理论上分析了两种方法的优劣,然后再用上述四种非线性预测模型对两种方法进行了仿真实验,并在以上非线性预测模型的基础上进行了多模式预测,仿真结果表明多模式预测能提高序列的有效预测率。
As the competition in frequency become more and more intensively in communication domain, the communication with fixed frequency suffers severe threats. In order to guarantee our communication, a new anti-Jamming communication system emerges, namely Frequency Hopping Communication System(FHCS). Duing to its outstanding counter-interference capability and Multiple Access properties, FHCS not only becomes popular in military communication, such as SINCGARS and JTIDS, but also is widely used in the civil mobile communications, including GSM, HomeRF and Bluetooth.
FH communication has anti-scout and anti-jamming capability by escaping the jamming. It can work when the frequency hops irregularly and quickly, which can’t be intercepted, recognised and interfered easily. It is interfered only when the signal frequency equals FH signal frequency. So the researches on anti-jamming and jamming of FHCS are a very important part of electronic warfare.The FH sequences which are used to control the frequency affects the anti-jamming capability crucially.
In order to improve the anti-jamming and jamming capability of FHCS, the main work is to predict FH Sequence in the dissertation, including the following several aspects.
1) The basic principle of FHCS’s and FH sequences is presented, the components of the system are described detailedly, FH mutiple access and synchronization methods are summarized, the design for FH sequences is studied. At last, computer simulations are done.
2) The construction principle of some familiar FH Sequences is analyzed. A novel predicting method aiming at the FH Sequences producted by shift-register is proposed. Computer simulations show the feasibility and efficiency of the method.
3) Several nonlinear predicting methods are analyzed and studied. BP Neural Network, RBF Neural Network, Bernstein polynomial, Volterra adaptive filter are used for predicting. The advantages and disadvantages of each predicting method in predicting different kinds of sequences are analyzed.
4) Multi-step and multi-mode forecasting for chaotic time series are researched. Compare the performance of two multi-step forecasting methods according to theory analysis and experimental results. Multi-mode forecasting based on the above four nonlinear predicting methods is made, and the simulation results indicate that effective forecasting probability can be increased.
引文
[1]梅文华,杨义先.跳频通信地址编码理论.北京:国防工业出版社,1996:1-2
[2]沈允春.扩谱技术.北京:国防工业出版社,1995:2-3
[3]张瑞.快速跳频系统研究及其基带实现:[硕士学位论文],西安:西北工业大学,2005:3-5
[4]陈新渝.无碰撞区跳频序列设计及其跳频通信系统仿真研究:[硕士学位论文],成都:西南交通大学,2-3
[5]王平军,徐敬,杨新友.对跳频通信系统干扰方法的研究.舰船电子对抗,Vol.29 No.5, Oct.2006:30-32
[6]郝威,杨露菁.跳频技术的发展及其干扰对策.舰船电子对抗,Vol.27 No.4,Aug.2004:10-11
[7]甘建超,肖先赐.混沌时间序列基于相空间邻域点的非线性多步自适应预测.物理学报,Vol.52,No.12,Dec.2003:2995-3000
[8]李文化,王智顺,何振亚.用于跳频多址通信的混沌跳频码.通信学报,1996:17-21
[9]郭双冰.肖先赐.几种跳频码的混沌动力学特性及预测分析.系统工程与电子技术,2000:29-32
[10]肖先赐.混沌信号非线性预测方法及应用.通信对抗,2006:3-7
[11]梅文华.跳频通信.国防工业出版社.2005:8-9
[12]查光明,熊贤祚.扩频通信.西安:西安电子科技大学出版社.2004:37-45
[13]沈允春.扩谱技术.北京:国防工业出版社,1995:6-13
[14]梅文华.跳频通信.国防工业出版社.2005:28-45
[15]梅文华,杨义先.跳频通信地址编码理论.北京:国防工业出版社,1996:49-114
[16]袁坚,肖先赐.关于对抗混沌跳频通信的研究.信号处理.1998:308-312
[17]于振华.基于在线小波支持向量回归的混沌时间序列预测.物理学报.2006:1659-1664
[18] Ling Cong and Sun Songgeng, Chaotic frequency hopping sequences, IEEE Trans. Comm., 1998, 46(11): 1433-1437
[19] Hongbin Chen, Jiuchao Feng. Generation of Chaotic Frequency Hopping Codes and its Application in Communications. IEEE, 2006:1-4
[20] B. F. Cheng, W. C. Li, Z. D. Li. A Method for Generating Chaotic Frequency Hopping Sequences Based on Queuing Theory and their Performance Analysis. Journal of Nankai University, China, vol. 34, June 2001:71-75
[21] S. Q. Hao, J. M. Cai. Characteristics of FH Sequences Based on a Chaotic Map and their Realizations. Radio Communications Technology, China, vol. 25, June 1999:25-28
[22] Y. P. Ji, G. R. Hou, W. C. Li. The Research of Chaotic FH Sequences. Journal of Nankai University, China, vol. 36, June 2003:63-67
[23] Mi Liang, Tang Gang. Design of FH Sequences Based on Chaotic Map. IEEE,2005:1422-1425
[24]李相东,张金善.跳频电台的同步技术.电子工程师,1996,1期,30-35
[25]戴敏.跳频通信技术及其应用与发展.2000,64期,40-43
[26]冯莉芳.基于无碰撞区码跳频系统准同步组网方案.西南交通大学学报,2004(6):776-779
[27]弹俊锋.自适应跳频通信系统仿真研究:[硕士学位论文],哈尔滨:哈尔滨工程大学.2005
[28]万哲先.代数和编码.北京:高等教育出版社,2007:257-277
[29] JOVAN DJ. On the Linear Complexity of Functions of Periodic GF(q) Sequences. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 35, No. 1, January 1989:69-75
[30] JAMES L. MASSEY. Shift-Register Synthesis and BCH Decoding. IEEE TRANSACTIONS ON INFORMATION THEORY, No. 1, January 1969:122-127
[31]张森,肖先赐.混沌时间序列全局预测新方法-连分式法.物理学报,2005: 5062-5068
[32]张家树,肖先赐.混沌时间序列的Volterra自适应预测.物理学报,2000,49(3):403-408
[33] Min Han,Jianhui Xi. Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network. IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL.52, 2004:3409-3416
[34] Zhengjun Liu, Hongming Yang, Mingyong Lai. Electricity Price Forecasting Model Based on Chaos Theory. IEEE, IPEC 2005:1-5
[35] Yonghong Chen, Jiyin Zhao, Deng Juan. Traffic Flow Forecasting Algorithm Research Based on Chaos Theory. IEEE, Proceedings of the 6th World Congress, 2006:8616-8620
[36]叶中行.混沌时间序列的区间预测.上海交通大学学报.1997:31(2):7-15
[37] S. V. Fridman, K. C. Yeh and S. J. Franke, Linear and nonlinear prediction techniques for short-term forecasting of HF fading signals, Radio Science, 1997, 22(3): 989-998
[38] Zhang Jia-Shu and Xiao Xian-Ci, Predicting chaotic time series using recurrent neural network, Chinese Physics Letters, 2000, 17(2): 88-90
[39] T. Koh, E. J. Powers, Second-order Volterra filtering and its application to nonlinear system identification, IEEE Trans. ASSP,33(6): 1445-1455
[40] S. Im and E. J. Powers, A third-order frequency-domain adaptive Volterra filter, IEEE Signal Processing Lett., 1997, 4(2): 75-78
[41] Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design.2002:197-221
[42]陈善广,鲍勇.BP神经网络学习算法研究.应用基础与工程科学学报,1995
[43]况爱武,黄中祥.基于RBF神经网络的短时交通流预测.系统工程,2004
[44] Chen S, CFN Cowan,P M Grant.Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks[J].IEEE Transactions on Neural Networks,1991;2(2):302~309
[45] Mo G D and Liu K D 2003 Methods of Approximation of Functions(Beijing: Science Press)(in Chinese)[莫国端,刘开第.函数逼近论方法.北京:科学出版社,2003:11-22]
[46]茅于海、苗家林.自适应预测滤波器的一种新算法.电子学报,1983
[47]孟轻纺,张强,牟文英.混沌时间序列多步自适应预测方法.物理学报,2006
[48]李洪涛,郝士琦,王磊.基于混沌理论的跳频频率多步自适应预测.通信对抗,2006
[2]沈允春.扩谱技术.北京:国防工业出版社,1995:2-3
[3]张瑞.快速跳频系统研究及其基带实现:[硕士学位论文],西安:西北工业大学,2005:3-5
[4]陈新渝.无碰撞区跳频序列设计及其跳频通信系统仿真研究:[硕士学位论文],成都:西南交通大学,2-3
[5]王平军,徐敬,杨新友.对跳频通信系统干扰方法的研究.舰船电子对抗,Vol.29 No.5, Oct.2006:30-32
[6]郝威,杨露菁.跳频技术的发展及其干扰对策.舰船电子对抗,Vol.27 No.4,Aug.2004:10-11
[7]甘建超,肖先赐.混沌时间序列基于相空间邻域点的非线性多步自适应预测.物理学报,Vol.52,No.12,Dec.2003:2995-3000
[8]李文化,王智顺,何振亚.用于跳频多址通信的混沌跳频码.通信学报,1996:17-21
[9]郭双冰.肖先赐.几种跳频码的混沌动力学特性及预测分析.系统工程与电子技术,2000:29-32
[10]肖先赐.混沌信号非线性预测方法及应用.通信对抗,2006:3-7
[11]梅文华.跳频通信.国防工业出版社.2005:8-9
[12]查光明,熊贤祚.扩频通信.西安:西安电子科技大学出版社.2004:37-45
[13]沈允春.扩谱技术.北京:国防工业出版社,1995:6-13
[14]梅文华.跳频通信.国防工业出版社.2005:28-45
[15]梅文华,杨义先.跳频通信地址编码理论.北京:国防工业出版社,1996:49-114
[16]袁坚,肖先赐.关于对抗混沌跳频通信的研究.信号处理.1998:308-312
[17]于振华.基于在线小波支持向量回归的混沌时间序列预测.物理学报.2006:1659-1664
[18] Ling Cong and Sun Songgeng, Chaotic frequency hopping sequences, IEEE Trans. Comm., 1998, 46(11): 1433-1437
[19] Hongbin Chen, Jiuchao Feng. Generation of Chaotic Frequency Hopping Codes and its Application in Communications. IEEE, 2006:1-4
[20] B. F. Cheng, W. C. Li, Z. D. Li. A Method for Generating Chaotic Frequency Hopping Sequences Based on Queuing Theory and their Performance Analysis. Journal of Nankai University, China, vol. 34, June 2001:71-75
[21] S. Q. Hao, J. M. Cai. Characteristics of FH Sequences Based on a Chaotic Map and their Realizations. Radio Communications Technology, China, vol. 25, June 1999:25-28
[22] Y. P. Ji, G. R. Hou, W. C. Li. The Research of Chaotic FH Sequences. Journal of Nankai University, China, vol. 36, June 2003:63-67
[23] Mi Liang, Tang Gang. Design of FH Sequences Based on Chaotic Map. IEEE,2005:1422-1425
[24]李相东,张金善.跳频电台的同步技术.电子工程师,1996,1期,30-35
[25]戴敏.跳频通信技术及其应用与发展.2000,64期,40-43
[26]冯莉芳.基于无碰撞区码跳频系统准同步组网方案.西南交通大学学报,2004(6):776-779
[27]弹俊锋.自适应跳频通信系统仿真研究:[硕士学位论文],哈尔滨:哈尔滨工程大学.2005
[28]万哲先.代数和编码.北京:高等教育出版社,2007:257-277
[29] JOVAN DJ. On the Linear Complexity of Functions of Periodic GF(q) Sequences. IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 35, No. 1, January 1989:69-75
[30] JAMES L. MASSEY. Shift-Register Synthesis and BCH Decoding. IEEE TRANSACTIONS ON INFORMATION THEORY, No. 1, January 1969:122-127
[31]张森,肖先赐.混沌时间序列全局预测新方法-连分式法.物理学报,2005: 5062-5068
[32]张家树,肖先赐.混沌时间序列的Volterra自适应预测.物理学报,2000,49(3):403-408
[33] Min Han,Jianhui Xi. Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network. IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL.52, 2004:3409-3416
[34] Zhengjun Liu, Hongming Yang, Mingyong Lai. Electricity Price Forecasting Model Based on Chaos Theory. IEEE, IPEC 2005:1-5
[35] Yonghong Chen, Jiyin Zhao, Deng Juan. Traffic Flow Forecasting Algorithm Research Based on Chaos Theory. IEEE, Proceedings of the 6th World Congress, 2006:8616-8620
[36]叶中行.混沌时间序列的区间预测.上海交通大学学报.1997:31(2):7-15
[37] S. V. Fridman, K. C. Yeh and S. J. Franke, Linear and nonlinear prediction techniques for short-term forecasting of HF fading signals, Radio Science, 1997, 22(3): 989-998
[38] Zhang Jia-Shu and Xiao Xian-Ci, Predicting chaotic time series using recurrent neural network, Chinese Physics Letters, 2000, 17(2): 88-90
[39] T. Koh, E. J. Powers, Second-order Volterra filtering and its application to nonlinear system identification, IEEE Trans. ASSP,33(6): 1445-1455
[40] S. Im and E. J. Powers, A third-order frequency-domain adaptive Volterra filter, IEEE Signal Processing Lett., 1997, 4(2): 75-78
[41] Martin T. Hagan, Howard B. Demuth, Mark H. Beale. Neural Network Design.2002:197-221
[42]陈善广,鲍勇.BP神经网络学习算法研究.应用基础与工程科学学报,1995
[43]况爱武,黄中祥.基于RBF神经网络的短时交通流预测.系统工程,2004
[44] Chen S, CFN Cowan,P M Grant.Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks[J].IEEE Transactions on Neural Networks,1991;2(2):302~309
[45] Mo G D and Liu K D 2003 Methods of Approximation of Functions(Beijing: Science Press)(in Chinese)[莫国端,刘开第.函数逼近论方法.北京:科学出版社,2003:11-22]
[46]茅于海、苗家林.自适应预测滤波器的一种新算法.电子学报,1983
[47]孟轻纺,张强,牟文英.混沌时间序列多步自适应预测方法.物理学报,2006
[48]李洪涛,郝士琦,王磊.基于混沌理论的跳频频率多步自适应预测.通信对抗,2006