卫星USB测控体制下信号特征参数的分析与识别
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摘要
卫星是现代信息网络中的重要节点,是推动全球信息化的重要手段。卫星测控通信系统是卫星与地球站或卫星与其它卫星之间进行信息交流的纽带,是卫星实现信息获取、信息传输和信息控制等功能的技术支持和功能保障。卫星统一S频段测控体制在军事和民用领域均得到广泛应用。开展卫星信号参数自动识别技术的研究,既可以用于自适应信号检测,又可以用于信息攻防中的信号侦收。因此,无论从理论角度还是从应用角度来看,都具有重要的学术意义,同时也具有极为重要的国防应用价值。
与软件无线电技术相结合是信号识别技术发展的新方向。基于软件无线电的卫星信号检测识别系统以开放性硬件为通用平台,用可升级、可重置的不同应用软件来实现信号的自动识别。以此为前提,如何在缺少先验知识的条件下保证参数识别的实时性、稳定性和可靠性日益成为国内外学者关注的焦点。
在此背景下,本文以卫星测控信号特征参数识别为研究对象,研究内容包括卫星测控信号去噪、单通道副载波欠定盲分离和测控信号调制参数盲估计三个关键技术。本文从理论分析、算法仿真以及硬件实现三个层面对上述问题展开了系统而深入地研究。本文的主要研究内容如下所述:
首先,论文对卫星测控信号去噪进行了研究。信号去噪属于参数识别的预处理,其目的是减小链路噪声的影响,提高参数识别的准确性和可靠性。为了降低算法对信号先验知识的依赖度,本文选择小波系数阈值法作为信号去噪的基本方法。在简要介绍了阈值法的基本原理和算法结构之后,对影响算法性能的几个关键问题进行了研究:一是依据小波基的特性参数,选出适用于本课题的最优小波基,并引入重构因子对所选小波基重构信号的能力进行评价;二是采用均值逼近法降低噪声方差,弱化噪声对阈值选取的影响,整体上减小了重构信号与原始信号之间的偏差;最后选取无偏似然阈值准则实现对阈值的自适应选择,并引入一种新的阈值函数对小波系数进行迭代处理。仿真结果说明了本文提出的自适应小波阈值去噪算法具有较好的去除白噪声的效果,为后续卫星测控副载波信号盲分离研究奠定了基础。
其次,论文介绍了副载波分离与参数估计涉及到的粒子滤波相关的理论知识,为后续章节研究分离与参数识别算法奠定基础,其内容包括贝叶斯滤波原理、蒙特卡罗方法及粒子滤波的基本思想,最后,介绍了粒子滤波算法中存在的退化问题并总结了粒子滤波的基本算法结构。从对粒子滤波理论的分析中了解到粒子滤波算法不仅适用于盲信号、非线性等极端条件,并且可以通过未知信号和参数的联合估计同时解决副载波分离与参数识别这两个问题,降低了算法复杂度。
再次,论文研究了卫星USB测控副载波盲识别问题。卫星测控副载波盲识别问题包括分离和参数估计两部分内容。通过对卫星测控副载波盲识别问题的分析可知卫星USB测控体制采用的频分复用技术决定了副载波盲分离属于特殊的单通道欠定盲源分离问题。本文引入粒子滤波的思想,提出了一种基于粒子滤波的副载波信号盲识别算法。将盲识别问题转化成了未知参数和信息符号的联合估计,解决了单通道欠定盲源分离和参数估计两个问题。在运用粒子滤波算法时,为了克服粒子退化问题,依据卫星信号状态空间的特点,在两个方面对粒子滤波盲识别算法进行改进,通过辅助变量粒子滤波算法将最新的观测值引入重要性函数中,同时,在重采样过程中,提出了一种基于粒子群优化思想的的粒子整体优化算法,提高了粒子的利用效率。本章最后,研究了测控副载波码速率识别算法。本文采用了对信噪比不敏感、无需先验知识的小波变换算法进行码速率的盲估计。为提高小波变换的分析性能,本文设计了一种粗细结合的两步估计算法,即先利用快速算法进行码速率粗估计,然后根据粗估计的结果选择小波尺度,再进行码速率细估计。仿真实验表明:本文算法其估计性能优于单一尺度算法。
最后,论文建立了一个测控信号识别试验系统,将卫星信号参数识别算法移植到硬件平台上。在对实验系统的组成部分进行介绍之后,分析了算法在硬件平台中实现的关键问题,对测控副载波盲识别算法进行改进,给出了测控副载波参数识别硬件平台验证算法。然后,通过CORTEX测控终端生成仿真信号,对移植到硬件平台上的算法进行测试。本文将系统与卫星测控地面站相连,实地实时对接收的某一在轨卫星信号进行识别,验证了算法的工程可行性和有效性。
Satellites are key hubs in modern information networks, and they are important means to promote global information realization. A Satellite monitoring and control communication system is the link through which information exchange between a satellite and the earth station or between a satellite and other satellites can be done, it is the technical and functional support for the information acquisition, information transmission and information control functions of a satellite. Unified S-band (USB) satellite measurement and control system is widely use in military and civilian applications. Based on this background, satellite single parameter automatic identification technique is researched, which can be applied to adaptive signal detection and signal reconnaissance receiving for information offense and defense. For this reason, it has important academic, national defense and social significance either from a theoretical point of view or from the application point of view.
Combining with Software Radio technique is a new tendency of development of Signal Recognition technique. A satellite signal detection and recognition system that is based on software radio uses open hardware as a universal platform, implementing automatic signal recognition by software that can be updated and reset. Based on this, to ensure the real-time performance, stability and reliability with little priori knowledge has become the focus of attentions of domestic and foreign researchers.
Firstly, satellite signal noise reduction techniques are studied in this paper. Signal noise reduction is a preprocessing for parameter identification, its goal is to reduce the impacts of the satellite link noise, to improve the accuracy and reliability of parameter identification. In this paper, the source of the noise in satellite measuring and control link is analyzed, development status of noise control technique is introduced; on this basis, weights of influence of different types of noise are evaluated, the satellite channel is simplified to a channel with Additive White Gaussian Noise(AWGN). In order to reduce the algorithm’s dependence on priori knowledge of signal, wavelet coefficients threshold method is adopted as the basic method of signal noise reduction. After a brief introduction of the basic principle and structure of threshold method, several key issues that affect the performance of the algorithm are studied: first, according to the characteristic parameters of wavelet bases, choosing appropriate optimal wavelet base, and by using reconstruction factors the reconstruction ability of the chosen wavelet base is evaluated; second, mean value approximation method is used to reduce the variance of noise, in order to weaken the impact of noise on the selection of threshold value, this can generally reduce the difference between the reconstructed signal and the original signal; in the end, unbiased maximum-likelihood threshold criterion is used for adaptive threshold selection, and a new threshold value function is introduced to do iterative processing on the wavelet coefficients.
Secondly, in this paper, some principles and knowledge of particle filters which are related to sub-carrier separation and parameter estimation are introduced, this forms the base of separation and parameter recognition algorithms studied in the following chapters, which includes Bayesian Filter principle, Monte-Carlo Method and basic idea of Particle Filters, in the end, the degeneration problem in particle filters is introduced and the structure of basic particle filter algorithm is summarized.
Thirdly, blind recognition of USB satellite measuring and control sub-carrier is studied. The Blind Recognition of satellite sub-carrier consists of two parts, namely Separation and Parameter Estimation. In this paper, the idea of particle filters is introduced, and a sub-carrier signal blind recognition algorithm based on particle filters is developed. The blind recognition problem can be transformed into a joint-estimation problem of unknown parameters and information symbols, the single channel underdetermined blind source separation and parameter estimation can be done at the same time. In the application of particle filters, in order to overcome the degeneration problem, taken into consideration of the characteristics of the satellite signal state space, the particle filter blind recognition algorithm can be improved in two ways, using auxiliary variable particle filter algorithm to introduce new observation values into the importance function, at the same time, during the re-sampling process, a particle global optimization algorithm based on particle group optimization is presented, which will improve the utilization efficiency of particles. At the end of this chapter, measuring and control sub-carrier code rate identification algorithms are studied. Wavelets transform algorithms, which are not sensitive to SNR and don’t require priori knowledge, are used for code rate blind estimation. In order to improve the performance of wavelets transform, a rough-and-precise-integrated two-step estimation algorithm is designed, it first uses fast algorithms to give a rough estimation of code rate, choosing wavelet scale according to the result of rough estimation, and then it can do precise estimation on the code rate. Simulation result shows: the performance of the algorithm shown in this paper is better than the performance of single-scale algorithms.
Lastly, a TT&C sub-carrier signals recognition test system is emplyed and a simplified TT&C sub-carrier signals recognition algorithm is transferred to the hardware board. The performance on the hardware board is deeply investigated using the signal source generator CORTEX. Furthermore, the recognition results of an in-orbit model satellite are given using TT&C sub-carrier signals recognition test system and remote sensing and controlling earth station in Harbin Institute of Technology, which proves the engineering validity of the algorithm.
与软件无线电技术相结合是信号识别技术发展的新方向。基于软件无线电的卫星信号检测识别系统以开放性硬件为通用平台,用可升级、可重置的不同应用软件来实现信号的自动识别。以此为前提,如何在缺少先验知识的条件下保证参数识别的实时性、稳定性和可靠性日益成为国内外学者关注的焦点。
在此背景下,本文以卫星测控信号特征参数识别为研究对象,研究内容包括卫星测控信号去噪、单通道副载波欠定盲分离和测控信号调制参数盲估计三个关键技术。本文从理论分析、算法仿真以及硬件实现三个层面对上述问题展开了系统而深入地研究。本文的主要研究内容如下所述:
首先,论文对卫星测控信号去噪进行了研究。信号去噪属于参数识别的预处理,其目的是减小链路噪声的影响,提高参数识别的准确性和可靠性。为了降低算法对信号先验知识的依赖度,本文选择小波系数阈值法作为信号去噪的基本方法。在简要介绍了阈值法的基本原理和算法结构之后,对影响算法性能的几个关键问题进行了研究:一是依据小波基的特性参数,选出适用于本课题的最优小波基,并引入重构因子对所选小波基重构信号的能力进行评价;二是采用均值逼近法降低噪声方差,弱化噪声对阈值选取的影响,整体上减小了重构信号与原始信号之间的偏差;最后选取无偏似然阈值准则实现对阈值的自适应选择,并引入一种新的阈值函数对小波系数进行迭代处理。仿真结果说明了本文提出的自适应小波阈值去噪算法具有较好的去除白噪声的效果,为后续卫星测控副载波信号盲分离研究奠定了基础。
其次,论文介绍了副载波分离与参数估计涉及到的粒子滤波相关的理论知识,为后续章节研究分离与参数识别算法奠定基础,其内容包括贝叶斯滤波原理、蒙特卡罗方法及粒子滤波的基本思想,最后,介绍了粒子滤波算法中存在的退化问题并总结了粒子滤波的基本算法结构。从对粒子滤波理论的分析中了解到粒子滤波算法不仅适用于盲信号、非线性等极端条件,并且可以通过未知信号和参数的联合估计同时解决副载波分离与参数识别这两个问题,降低了算法复杂度。
再次,论文研究了卫星USB测控副载波盲识别问题。卫星测控副载波盲识别问题包括分离和参数估计两部分内容。通过对卫星测控副载波盲识别问题的分析可知卫星USB测控体制采用的频分复用技术决定了副载波盲分离属于特殊的单通道欠定盲源分离问题。本文引入粒子滤波的思想,提出了一种基于粒子滤波的副载波信号盲识别算法。将盲识别问题转化成了未知参数和信息符号的联合估计,解决了单通道欠定盲源分离和参数估计两个问题。在运用粒子滤波算法时,为了克服粒子退化问题,依据卫星信号状态空间的特点,在两个方面对粒子滤波盲识别算法进行改进,通过辅助变量粒子滤波算法将最新的观测值引入重要性函数中,同时,在重采样过程中,提出了一种基于粒子群优化思想的的粒子整体优化算法,提高了粒子的利用效率。本章最后,研究了测控副载波码速率识别算法。本文采用了对信噪比不敏感、无需先验知识的小波变换算法进行码速率的盲估计。为提高小波变换的分析性能,本文设计了一种粗细结合的两步估计算法,即先利用快速算法进行码速率粗估计,然后根据粗估计的结果选择小波尺度,再进行码速率细估计。仿真实验表明:本文算法其估计性能优于单一尺度算法。
最后,论文建立了一个测控信号识别试验系统,将卫星信号参数识别算法移植到硬件平台上。在对实验系统的组成部分进行介绍之后,分析了算法在硬件平台中实现的关键问题,对测控副载波盲识别算法进行改进,给出了测控副载波参数识别硬件平台验证算法。然后,通过CORTEX测控终端生成仿真信号,对移植到硬件平台上的算法进行测试。本文将系统与卫星测控地面站相连,实地实时对接收的某一在轨卫星信号进行识别,验证了算法的工程可行性和有效性。
Satellites are key hubs in modern information networks, and they are important means to promote global information realization. A Satellite monitoring and control communication system is the link through which information exchange between a satellite and the earth station or between a satellite and other satellites can be done, it is the technical and functional support for the information acquisition, information transmission and information control functions of a satellite. Unified S-band (USB) satellite measurement and control system is widely use in military and civilian applications. Based on this background, satellite single parameter automatic identification technique is researched, which can be applied to adaptive signal detection and signal reconnaissance receiving for information offense and defense. For this reason, it has important academic, national defense and social significance either from a theoretical point of view or from the application point of view.
Combining with Software Radio technique is a new tendency of development of Signal Recognition technique. A satellite signal detection and recognition system that is based on software radio uses open hardware as a universal platform, implementing automatic signal recognition by software that can be updated and reset. Based on this, to ensure the real-time performance, stability and reliability with little priori knowledge has become the focus of attentions of domestic and foreign researchers.
Firstly, satellite signal noise reduction techniques are studied in this paper. Signal noise reduction is a preprocessing for parameter identification, its goal is to reduce the impacts of the satellite link noise, to improve the accuracy and reliability of parameter identification. In this paper, the source of the noise in satellite measuring and control link is analyzed, development status of noise control technique is introduced; on this basis, weights of influence of different types of noise are evaluated, the satellite channel is simplified to a channel with Additive White Gaussian Noise(AWGN). In order to reduce the algorithm’s dependence on priori knowledge of signal, wavelet coefficients threshold method is adopted as the basic method of signal noise reduction. After a brief introduction of the basic principle and structure of threshold method, several key issues that affect the performance of the algorithm are studied: first, according to the characteristic parameters of wavelet bases, choosing appropriate optimal wavelet base, and by using reconstruction factors the reconstruction ability of the chosen wavelet base is evaluated; second, mean value approximation method is used to reduce the variance of noise, in order to weaken the impact of noise on the selection of threshold value, this can generally reduce the difference between the reconstructed signal and the original signal; in the end, unbiased maximum-likelihood threshold criterion is used for adaptive threshold selection, and a new threshold value function is introduced to do iterative processing on the wavelet coefficients.
Secondly, in this paper, some principles and knowledge of particle filters which are related to sub-carrier separation and parameter estimation are introduced, this forms the base of separation and parameter recognition algorithms studied in the following chapters, which includes Bayesian Filter principle, Monte-Carlo Method and basic idea of Particle Filters, in the end, the degeneration problem in particle filters is introduced and the structure of basic particle filter algorithm is summarized.
Thirdly, blind recognition of USB satellite measuring and control sub-carrier is studied. The Blind Recognition of satellite sub-carrier consists of two parts, namely Separation and Parameter Estimation. In this paper, the idea of particle filters is introduced, and a sub-carrier signal blind recognition algorithm based on particle filters is developed. The blind recognition problem can be transformed into a joint-estimation problem of unknown parameters and information symbols, the single channel underdetermined blind source separation and parameter estimation can be done at the same time. In the application of particle filters, in order to overcome the degeneration problem, taken into consideration of the characteristics of the satellite signal state space, the particle filter blind recognition algorithm can be improved in two ways, using auxiliary variable particle filter algorithm to introduce new observation values into the importance function, at the same time, during the re-sampling process, a particle global optimization algorithm based on particle group optimization is presented, which will improve the utilization efficiency of particles. At the end of this chapter, measuring and control sub-carrier code rate identification algorithms are studied. Wavelets transform algorithms, which are not sensitive to SNR and don’t require priori knowledge, are used for code rate blind estimation. In order to improve the performance of wavelets transform, a rough-and-precise-integrated two-step estimation algorithm is designed, it first uses fast algorithms to give a rough estimation of code rate, choosing wavelet scale according to the result of rough estimation, and then it can do precise estimation on the code rate. Simulation result shows: the performance of the algorithm shown in this paper is better than the performance of single-scale algorithms.
Lastly, a TT&C sub-carrier signals recognition test system is emplyed and a simplified TT&C sub-carrier signals recognition algorithm is transferred to the hardware board. The performance on the hardware board is deeply investigated using the signal source generator CORTEX. Furthermore, the recognition results of an in-orbit model satellite are given using TT&C sub-carrier signals recognition test system and remote sensing and controlling earth station in Harbin Institute of Technology, which proves the engineering validity of the algorithm.
引文
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32 Jang G.J., Lee T.W. A maximum likelihood approach to single channel source separation. Journal of Machine Learning Research. 2004,4(7-8):1365~1392
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34 Benaroya L., Bimbot F., Gribonval R. Audio Source Separation With a Single Sensor. IEEE Trans.on Audio, Speech and Language Processing. 2006,14(1):191~199
35 Veterbi A J, Veterbi A M. Nonlinear estimation of PSK-Modulated carrier phase with application to burst digital transmissions. IEEE Trans Inform Theory. 1983,IT-29:543~551
36 Mazzenga F, Corazza G E. Blind least-squares estimation of carrier phase, Doppler shift, and Doppler rate for M-PSK burst transmission. IEEE Communications Letters. 1998,2(3):73~75
37李晶,朱江,张尔扬,沈荣骏.高速8PSK调制信号的频率捕获及跟踪算法研究[J].信号处理. 2005(1):66~69
38 Bellini S. Digital frequency estimators for M-PSK. Proceedings of the 3rd European Conference on Satellite Communications 1993. Stevenage, England:IEE. 1993,(381):362~366
39 Mounir Ghogho, Ananthmm Swami, Tariq Durrani. Blind estimation of frequency offset in the presence of unknown multipath. 2000 IEEE International Conference on Personal Wireless Communications. Piscataway, NJ, USA: IEEE, 2000,104~108
40 Dae-Ki Hong, Sung-Jin Kang, Min-Chul Ju, Yong-Sung Kim, Jin-Woong Cho. Low-complexity joint estimation of frequency offset and carrier phase for M-ary PSK. Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2004. Piscataway, NJ,USA: IEEE. 2004,639~644
41王立乾,赵国庆,郑文秀.基于现代谱估计的PSK信号频率估计方法.现代电子技术. 2003(23):44~47
42 Gardner W A. Signal interception: a unifying theoretical framework for featuredetection. IEEE Trans. Commun. 1988, 36(8):897~906
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44 M azet L, Loubaton P. Cyclic correlation based symbol rate estimation Proceedings of Thirty-Third Asilomar Conference on Signals, Systems and Computers. California.IEEE Press. 1999:1008~1012
45 Mammone R H, Rothaker R J, Podilehuk C I.Estimation of carrier frequency, modulation type and bit rate of an unknown modulated signal. ICC, Seattle, WA. 1987:1006~1012
46蒋鹏.小波理论在信号去噪和数据压缩中的应用研究.浙江大学博士论文. 2004:63~64
47飞思科技产品研发中心编著.小波分析理论与MATLAB7实现.电子工业出版社. 2005
48 Hagiwara M, Nakagawa M. Automatic estimation of an input signal type. Technical Report IEICE, GLOBECOM Tokyo’87. New York. 1987(1):254~258
49 Carl Taswell.The what, how and why of wawelet shrinkage denoising. Computing in Science and Engineering. 2000,2(3):12~19
50 X. Ma, C.Zhou, I. T.Kemp. Automated Wavelet Selection and Thresholding for PD Detection. IEEE Electrical Insulation Magazine, March/April 2002(l8):2
51李月琴,栗苹,闫晓鹏,陈慧敏.无线电引信信号去噪的最优小波基选择.北京理工大学学报. 2008,28(8):724~725
52 DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika,1992,(81)425~455
53 Donoho D L. De-noising by soft-thresholding. IEEE Trans on Information Theory.1995,41(3):613~627
54 CCSDS 411.0-G-3. Radio Frequency and Modulation Systems-Part 1: Earth Stations. Green Book. 1997
55郝岩.航天测控网.国防工业出版社.2004: 91~103,158~170
56范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别.通信学报. 2004, 25(1): 140~149
57钟兴旺,陈豪,易克初.卫星测控信号的调制类型自动识别算法.中国空间科学技术.2003,6(3): 57~64
58 G.Lopez-Risueno, J.Grajal, O.A.Yeste-Jeda. Two Digital Receivers Based on Time-Frequency Analysis for Signal Interception. Radar Conference, 2003. Proceedings of the International 2003: 394~399
59吕铁军,魏平,肖先赐.基于分形和测度理论的信号调制识别.电波科学学报. 2001, 16(1): 123~127
60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, 23(1): 111~115
61张旻,程加兴.基于粒度计算和覆盖算法的信号样式识别.计算机工程与应用.2003, (24): 56~59
62 Yafeng Yao, Zailu Huang. Using Linear Smoothing to Improve the Modulation Recognition Performance. Proceedings of 2003 International Conference on Natural Language Processing and Knowledge Engineering. 2003: 84~88
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34 Benaroya L., Bimbot F., Gribonval R. Audio Source Separation With a Single Sensor. IEEE Trans.on Audio, Speech and Language Processing. 2006,14(1):191~199
35 Veterbi A J, Veterbi A M. Nonlinear estimation of PSK-Modulated carrier phase with application to burst digital transmissions. IEEE Trans Inform Theory. 1983,IT-29:543~551
36 Mazzenga F, Corazza G E. Blind least-squares estimation of carrier phase, Doppler shift, and Doppler rate for M-PSK burst transmission. IEEE Communications Letters. 1998,2(3):73~75
37李晶,朱江,张尔扬,沈荣骏.高速8PSK调制信号的频率捕获及跟踪算法研究[J].信号处理. 2005(1):66~69
38 Bellini S. Digital frequency estimators for M-PSK. Proceedings of the 3rd European Conference on Satellite Communications 1993. Stevenage, England:IEE. 1993,(381):362~366
39 Mounir Ghogho, Ananthmm Swami, Tariq Durrani. Blind estimation of frequency offset in the presence of unknown multipath. 2000 IEEE International Conference on Personal Wireless Communications. Piscataway, NJ, USA: IEEE, 2000,104~108
40 Dae-Ki Hong, Sung-Jin Kang, Min-Chul Ju, Yong-Sung Kim, Jin-Woong Cho. Low-complexity joint estimation of frequency offset and carrier phase for M-ary PSK. Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2004. Piscataway, NJ,USA: IEEE. 2004,639~644
41王立乾,赵国庆,郑文秀.基于现代谱估计的PSK信号频率估计方法.现代电子技术. 2003(23):44~47
42 Gardner W A. Signal interception: a unifying theoretical framework for featuredetection. IEEE Trans. Commun. 1988, 36(8):897~906
43 Dandawate A V, Giannakis G B. Statistical tests for presence of cyclostationarity. IEEE Trans. on Signal Processing. 1994,42(9):2355~2369
44 M azet L, Loubaton P. Cyclic correlation based symbol rate estimation Proceedings of Thirty-Third Asilomar Conference on Signals, Systems and Computers. California.IEEE Press. 1999:1008~1012
45 Mammone R H, Rothaker R J, Podilehuk C I.Estimation of carrier frequency, modulation type and bit rate of an unknown modulated signal. ICC, Seattle, WA. 1987:1006~1012
46蒋鹏.小波理论在信号去噪和数据压缩中的应用研究.浙江大学博士论文. 2004:63~64
47飞思科技产品研发中心编著.小波分析理论与MATLAB7实现.电子工业出版社. 2005
48 Hagiwara M, Nakagawa M. Automatic estimation of an input signal type. Technical Report IEICE, GLOBECOM Tokyo’87. New York. 1987(1):254~258
49 Carl Taswell.The what, how and why of wawelet shrinkage denoising. Computing in Science and Engineering. 2000,2(3):12~19
50 X. Ma, C.Zhou, I. T.Kemp. Automated Wavelet Selection and Thresholding for PD Detection. IEEE Electrical Insulation Magazine, March/April 2002(l8):2
51李月琴,栗苹,闫晓鹏,陈慧敏.无线电引信信号去噪的最优小波基选择.北京理工大学学报. 2008,28(8):724~725
52 DONOHO D L, JOHNSTONE I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika,1992,(81)425~455
53 Donoho D L. De-noising by soft-thresholding. IEEE Trans on Information Theory.1995,41(3):613~627
54 CCSDS 411.0-G-3. Radio Frequency and Modulation Systems-Part 1: Earth Stations. Green Book. 1997
55郝岩.航天测控网.国防工业出版社.2004: 91~103,158~170
56范海波,杨志俊,曹志刚.卫星通信常用调制方式的自动识别.通信学报. 2004, 25(1): 140~149
57钟兴旺,陈豪,易克初.卫星测控信号的调制类型自动识别算法.中国空间科学技术.2003,6(3): 57~64
58 G.Lopez-Risueno, J.Grajal, O.A.Yeste-Jeda. Two Digital Receivers Based on Time-Frequency Analysis for Signal Interception. Radar Conference, 2003. Proceedings of the International 2003: 394~399
59吕铁军,魏平,肖先赐.基于分形和测度理论的信号调制识别.电波科学学报. 2001, 16(1): 123~127
60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, 23(1): 111~115
61张旻,程加兴.基于粒度计算和覆盖算法的信号样式识别.计算机工程与应用.2003, (24): 56~59
62 Yafeng Yao, Zailu Huang. Using Linear Smoothing to Improve the Modulation Recognition Performance. Proceedings of 2003 International Conference on Natural Language Processing and Knowledge Engineering. 2003: 84~88
63 Quanwei Cai, Ping Wei, Xianci Xiao. A Digital Modulation Recognition Method. International Conference on Communications, Circuits and Systems (ICCCAS), 2004. 2004, (2): 863~866
64李晓松,黄陵.航天测控通信系统综合基带中频接收单元数字化方案.电讯技术.2001, (3):1
65顾斌,赵明忠.软件无线电中FM调制解调算法的DSP实现.电子工程师.2004, 30(5):1
66张利萍,徐昌庆,吴斌.软件无线电在卫星测控模拟器中的应用.飞行器测控学报.2006, 25(2):3
67刘娟花,李福德.基于小波变换的信号去噪研究.西安理工大学学报.2004, 20(3):1
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