软件无线电中调制信号识别方法的研究
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摘要
通信信号的调制识别是软件无线电的核心技术之一,任何一个通信信号必须确知该信号的调制方式及信号参数才能进行接收解调,本课题研究的目的是在未知调制信息的前提下,在多信号环境和有噪声干扰的条件下从接收信号中分析出通信信号的调制方式,并估计出相应的调制参数,为后续的解调及信号处理奠定基础。本课题研究的重要性在于针对传统的特定调制方式和带宽的单一型通信系统,将不适应目前的多调制、多服务的通信系统的要求。所以伴随通信信号的体制和调制样式的多样化发展,调制信号识别技术将在软件无线电的不断发展中起到十分重要的作用。
本文所作的主要工作:
(1)首先对软件无线电的结构和目前的关键技术进行了概括性阐述,综述了现有算法,分析了现有算法的特点。
(2)阐述了各种调制信号的基本原理,给出了AM、PM、FM、DSB、USB和LSB信号的时域及频谱仿真图,可以清楚的理解各模拟调制信号的特性。分析了数字调制信号2ASK、4ASK、2FSK、4FSK、2PSK、4PSK的瞬时幅度、瞬时相位和瞬时频率,为后面进行识别奠定基础,最后给出了基于瞬时特征的主要特征参数。
(3)为避免决策理论的固有缺陷,研究了基于高阶累积量的数字调制信号自动识别算法,该算法不需要先验知识且能有效避免高斯噪声的影响,对五种数字调制信号2ASK/2PSK、4ASK、4PSK、2FSK和4FSK的类间识别效果明显,仿真结果证明此算法能够在较低的信噪比下有效的实现分类的目的,并将提取的特征参数应用神经网络对算法进行了改进。
(4)将决策理论与高阶累积量相结合提取调制信号的特征参数,利用BP网络分类器对2ASK、4ASK、2FSK、4FSK、2PSK、4PSK和16QAM七种数字调制信号进行分类识别,仿真实验证明该方法具有较强的鲁棒性和实用性。
Any communication signals must inform the modulation mode and parameter of the signal to receive and demodulation. The aim of this thesis is that it can analyze the modulation types and estimate the modulation parameters of the communication signals from the received signals, Given that any modulation infomation is unknown, which is traveling in multi-signals and noise environment. This is very important for analyzing and processing signal. The importance of the thesis is that given modulation mode and bandwidth of traditional singularity communication system would no longer adapt the request of presently multi-modulates and multi-serves commucation system. With rapid development of communication technology, the system and modulation mannerof communication signals became more and more complicated and various. Modulation identification would be more effect in the development of software radio.
The main work in this paper can be summarized as follows:
1. This paper introduces the architecture of software radio and related theory, some key technologies of software radio are researched, then dissussed the algorithm of modulation and demodulation.
2. The common model of modulation signals is discussed. This part expatiates the frequency spectrum of analog signals AM、PM、FM、DSB、USB and LSB,it shows clearly the characteristic of analog signals. Analyse the instantaneous extent, instantaneous phase and instantaneous frequency of digital signals 2ASK、4ASK、2FSK、4FSK、2PSK and 4PSK. Lay a foundation for the after identification. At last expatiates the main character parameters based on instantaneous characters.
3. To avoid the fixed limitation of decision-theoretic, the paper introduce the identification algorithm based on higher-orde cumulants (HOC) of digital signals.the algorithm shows great effect in identifying five different digital modulated signals 2ASK/2PSK、4ASK、4PSK、2FSK and 4FSK. Simulations show that the algorithm is able to avoid the influence of gauss white noise, the character parameters would be used in the backward study in neural network identification algorithm.
4. In this paper we propose an automatic modulation recognition system to recognize seven digital signal classes as 2ASK、4ASK、2FSK、4FSK、2PSK、4PSK and 16QAM using decision-theoretic based feature set addition to statistical pattern based feature set with Back Propagation (BP) neural network. In order to verify the performance of the system, we carry out a large amount of emulation experiment from the neural networks. Computer simulation results show that the robustness and practicability of this recognition method.
本文所作的主要工作:
(1)首先对软件无线电的结构和目前的关键技术进行了概括性阐述,综述了现有算法,分析了现有算法的特点。
(2)阐述了各种调制信号的基本原理,给出了AM、PM、FM、DSB、USB和LSB信号的时域及频谱仿真图,可以清楚的理解各模拟调制信号的特性。分析了数字调制信号2ASK、4ASK、2FSK、4FSK、2PSK、4PSK的瞬时幅度、瞬时相位和瞬时频率,为后面进行识别奠定基础,最后给出了基于瞬时特征的主要特征参数。
(3)为避免决策理论的固有缺陷,研究了基于高阶累积量的数字调制信号自动识别算法,该算法不需要先验知识且能有效避免高斯噪声的影响,对五种数字调制信号2ASK/2PSK、4ASK、4PSK、2FSK和4FSK的类间识别效果明显,仿真结果证明此算法能够在较低的信噪比下有效的实现分类的目的,并将提取的特征参数应用神经网络对算法进行了改进。
(4)将决策理论与高阶累积量相结合提取调制信号的特征参数,利用BP网络分类器对2ASK、4ASK、2FSK、4FSK、2PSK、4PSK和16QAM七种数字调制信号进行分类识别,仿真实验证明该方法具有较强的鲁棒性和实用性。
Any communication signals must inform the modulation mode and parameter of the signal to receive and demodulation. The aim of this thesis is that it can analyze the modulation types and estimate the modulation parameters of the communication signals from the received signals, Given that any modulation infomation is unknown, which is traveling in multi-signals and noise environment. This is very important for analyzing and processing signal. The importance of the thesis is that given modulation mode and bandwidth of traditional singularity communication system would no longer adapt the request of presently multi-modulates and multi-serves commucation system. With rapid development of communication technology, the system and modulation mannerof communication signals became more and more complicated and various. Modulation identification would be more effect in the development of software radio.
The main work in this paper can be summarized as follows:
1. This paper introduces the architecture of software radio and related theory, some key technologies of software radio are researched, then dissussed the algorithm of modulation and demodulation.
2. The common model of modulation signals is discussed. This part expatiates the frequency spectrum of analog signals AM、PM、FM、DSB、USB and LSB,it shows clearly the characteristic of analog signals. Analyse the instantaneous extent, instantaneous phase and instantaneous frequency of digital signals 2ASK、4ASK、2FSK、4FSK、2PSK and 4PSK. Lay a foundation for the after identification. At last expatiates the main character parameters based on instantaneous characters.
3. To avoid the fixed limitation of decision-theoretic, the paper introduce the identification algorithm based on higher-orde cumulants (HOC) of digital signals.the algorithm shows great effect in identifying five different digital modulated signals 2ASK/2PSK、4ASK、4PSK、2FSK and 4FSK. Simulations show that the algorithm is able to avoid the influence of gauss white noise, the character parameters would be used in the backward study in neural network identification algorithm.
4. In this paper we propose an automatic modulation recognition system to recognize seven digital signal classes as 2ASK、4ASK、2FSK、4FSK、2PSK、4PSK and 16QAM using decision-theoretic based feature set addition to statistical pattern based feature set with Back Propagation (BP) neural network. In order to verify the performance of the system, we carry out a large amount of emulation experiment from the neural networks. Computer simulation results show that the robustness and practicability of this recognition method.
引文
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[32]Ananthram Swami,Brian M.Sadler.Hierarchical digital modulationclaasification using cumulants.IEEE Trans.Common,2000,(3):416-429.
[33]George Hatzichristos,Monique E Fargues.A hierarchical approach to theclassification of digital modulation types in multipath environments,Conference record of thethirty-fifth asilomar conferenceon.Signals,systems and computers,2001,(2):1494-1498.
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[36]韩钢,张文红,李建东等.基于高阶累积量和支撑矢量机的调制识别研究.系统工程与电子技术,2003,25(8),1007-1011.
[37]吕铁军,郭双冰,肖先赐.调制信号的分析特征研究.中国科学,2001,(6):508-513.
[38]吕铁军,郭双冰,肖先赐.基于复杂度特征的调制信号识别.通信学报,2002,21(1):111-115.
[39]任淑萍,王华奎等.软件无线电中调制信号的神经网络识别方法.太原理工大学学报,2006.
[40]黄春琳,邱玲,沈振康.数字调制信号的神经网络识别方法.国防科技大学学报,1999,21(2):58-61.
[41]王康利,谢建菲,赵兰华.基于神经网络的软件无线电的调制识别.计算机测量与控制,2004,12(9):877-894.
[42]M.Ibnkahla.Applications of neural networks to digital communications.Signal processing,2000,80:1185-1215.
[43]赵德群,吕振肃,孙伟.基于离散搜索神经网络的数字调制类型识别.甘肃工业大学学报,2002,28(3):7173.
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[02]范小骞.电子对抗中通信信号数字调制自动识别的研究.太原:太原理工大学硕士学位论文,2004.
[03]吕新正.基于多特征的通信信号调制识别研究.成都:电子科技大学硕士学位论文,2004.
[04]樊伟.通信信号自动调制识别中的分类器设计.成都:西南交通大学硕士学位论文,2005.
[05]张鸣.软件无线电接收机中信号调制样式识别算法的研究.西安:西安科技大学硕士学位论文,2005.
[06]韩鹍.软件无线电接收机中信号调制样式识别算法的研究.无锡:江南大学硕士学位论文,2005.
[07]Hsue.S.Z,Soliman.S.S.Automatic modulation recognition of digitally modulated signals.MILCOM'89,37(4):1-5.
[08]Hsue.S.Z,Soliman.S.S.Automatic modulation classification using statistical moments.IEE Proc.F Radar signal process,1990,137(6)459-464.
[09]骆骋.自组织神经网络实现无线数字信号识别.无线通信技术,2000,1(4):9-13.
[10]Assaleh.K,Farrell.K,Mammone.R.J.A new method of modulation classification for digitally modulated signals.MILCOM'92,2:712-716.
[11]Lu Mingquan,Xiao Xianci,Li lemin.Ar modeling based features extraction formultiple signals for modulation recognition,in Signal Processing Proceedings,4th International Conference,1998,21:385-388.
[12]戴威,王有政,王京.基于AR模型的调制盲识别方法.电子学报,2001,29(12): 1890-1892.
[13]Ho.C.K,W.Prokopiw,Chan.T.Y.Modulation identification by the wavelets,in Proc,SPIE,1993,2491:492-503.
[14]Ta.P.N.Awavelet packet approach to radio signal modulation classification.ICCS'94,Singapore,1994;1210-214.
[15]Ho.C.K,Liang Hong.Identification of digital modulation types using the wavelet transform.1999,1:427-431.
[16]Sangwoo Cho,Chong Hyun Lee,Jooh wan chun.Classification of digital modulations using the LPC.National Aerospace and Electronics Conference,Proceedings of the IEEE,2000,774-778.
[17]蒋盘林.小波变换技术及其在信号调制方式识别中的应用.电子对抗,2000:19-24.
[18]Soliman.S.S,Hsue.S.Z.Signal classification using statistical moments.IEEE Transactions on Communications,1992,40:908-916.
[19]Wei Dai,Wouzheng Wang,Jing Wang.Joint Power Estimation and Modulation Classification Using Second-and Higher Statistics.Wireless Communications and networking Conference,2002,1:155-158.
[20]胡延平,李广森,李纲,皇甫堪.利用参数统计方法自动识别数字调制信号.通信学报,2002,23(2):58-65.
[21]Gardener.W.A.Spectral Correlation of Modulated Signals PART-Analog Modulation.IEEE Trans Communication,1987,35584-594.
[22]Gardener.W.A.Spectral Correlation of Modulated Signals PART-Digital Modulation.IEEE Trans.Communication,1987,35595-601.
[23]Gardener.W.A,Spooner.C.W.Cyclic spectral analysis for signal detection and modulation recognition.MILCOM'98,2419-424.
[24]朱德君.谱相关理论在电子侦察中的应用.电子对抗,1995,1(2):43-57.
[25]韩国栋,蔡斌,邬江兴.调制分析与识别的谱相关方法.系统工程与电子技术,2001,23(3)34-36.
[26]吕杰,张胜付,张克等.移动通信信号自动调制识别的谱相关方法.电讯技术,1999,(2):9-13,17.
[27]赵星.通信信号调制样式识别技术.成都:电子科技大学硕士学位论文,2006.
[28]李翠芳.基于多层感知器调制信号识别的研究.太原:太原理工大学硕士学位论文,2006.
[29]Azzouz E E,Nandi A K.Automatic identification of digital modulation types.Signal Processing,1995,47(1):55-69.
[30]Swami A,Sadler B.M.Hierarchical digital modulation classification using cumulants.IEEE Transactions on Communication,March 2000,48(3):141-144.
[31]M.L.D.Wong.Automatic digital modulation recognition using spectral and statistical features with multi-layer perceptrons.IEEE,ISSPA,2001,8,390-393.
[32]Ananthram Swami,Brian M.Sadler.Hierarchical digital modulationclaasification using cumulants.IEEE Trans.Common,2000,(3):416-429.
[33]George Hatzichristos,Monique E Fargues.A hierarchical approach to theclassification of digital modulation types in multipath environments,Conference record of thethirty-fifth asilomar conferenceon.Signals,systems and computers,2001,(2):1494-1498.
[34]陈卫东,杨绍全.多径信道中MPSK信号的调制识别算法.通信学报,2002,23(6):14-21.
[35]陈卫东,杨绍全.利用累量不变量对MPSK信号分类.西安电子科技大学学报,2002,(2):229-232,240.
[36]韩钢,张文红,李建东等.基于高阶累积量和支撑矢量机的调制识别研究.系统工程与电子技术,2003,25(8),1007-1011.
[37]吕铁军,郭双冰,肖先赐.调制信号的分析特征研究.中国科学,2001,(6):508-513.
[38]吕铁军,郭双冰,肖先赐.基于复杂度特征的调制信号识别.通信学报,2002,21(1):111-115.
[39]任淑萍,王华奎等.软件无线电中调制信号的神经网络识别方法.太原理工大学学报,2006.
[40]黄春琳,邱玲,沈振康.数字调制信号的神经网络识别方法.国防科技大学学报,1999,21(2):58-61.
[41]王康利,谢建菲,赵兰华.基于神经网络的软件无线电的调制识别.计算机测量与控制,2004,12(9):877-894.
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