通信电台个体特征分析
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摘要
通信电台个体识别是近年来通信侦察领域一个重要研究课题，它主要根据各电台硬件差异在发射信号上表现出来的区别于其它个体的特征，判别信号来自哪个电台，进而实现电台跟踪，并为判定通信网络的组成等提供重要依据。通信电台个体识别的研究不同于传统的通信信号侦察，在传统的通信信号侦察过程中，研究的重点往往围绕通信电台传递的通信信息的获取或分析进行，来自硬件差异的个体特征往往被作为噪声而力求滤除或削弱其影响；但是，在研究电台信号个体识别时，通信信息的获取和分析都不再作为研究的重点，反而是这些体现电台之间个体差异的信号个体特征成为需要获取的信息。
通信电台个体特征的分析、研究在电台个体识别中至关重要，本文主要针对相同工作模式、相同型号下的不同电台，研究通信电台个体识别中这个关键环节——电台个体特征分析。论文研究了个体特征的基本理论，并从其产生机理出发，研究了电台不同工作状态下个体特征的分析方法，包括电台稳定工作状态下的噪声特性、杂散特性和频率特性，以及非稳定工作状态下电台暂态特征分析，并在非稳定工作状态下探讨了不同工作模式下电台个体识别的解决方案。文中提出、推导了一系列具有理论及实用价值的新算法，并通过实际电台数据和计算机仿真实验验证了所提算法的优良性能。归纳起来，本文的贡献主要包括以下几个方面：
1、全文从电台稳定工作状态和非稳定工作状态系统地研究了电台的个体特征分析方法，建立了通信电台个体识别基本框架。文中明确给出了通信信号个体特征的定义，即只有具有普遍性、唯一性、稳定性和可检测性的信号特征方可作为识别电台个体的个体特征。同时，系统、全面地分析了个体特征产生机理，为个体特征分析提供了理论基础。为衡量所选特征的分类能力，提出了一种评价特征集分离性能的指标——可分离指数，根据该指标的测试，可以择优选相关特征作为电台的个体特征，为后续分析提供了一个分析工具。
2、针对稳定工作状态下硬件差异在信号上的不同体现，论文从噪声特性、杂散特性和频率三方面进行了个体特征分析研究。
在噪声特性分析方面，分析了各电台噪声统计特性上的差异，研究了噪声功率估计算法，提出了一种基于最小二乘法一特征值分解(LS-EVD)的噪声功率估计的改进算法，该算法不需要任何先验知识，在中等信噪比条件下噪声估计性良好，
Individual communication transmitter identification is an important issue in the field of communication reconnaissance in recent years. With the characteristics reflected on signal by the difference of the transmitter hardware, this issue focuses on seeking the source of the received signal. So the transmitter tracking can be realized and a significant thread can be provided for the determination of communication network construction. The study of this subject is different from the study of the traditional communication reconnaissance. Analyzing and intercepting the transmitted information are important subjects of the traditional communication reconnaissance but they are not the focuses of the individual transmitter identification, and the individual characteristics from the difference of the hardware will be removed or diminished as noise when studying the traditional communication reconnaissance but will be the emphasis when studying transmitter individual identification.
The individual transmitter character analysis is vital for communication transmitter identification. Aiming at the different transmitters of the same type and working mode, the individual transmitter character analysis is taken in this dissertation. With the basic theory and the mechanism, researching work is taken on the individual character analyzing method of different working mode, the stable working characters including the noise character, the spur character and the frequency character, and the transient character analysis of unstable working mode. A solution is proposed aiming at transmitter identification of different working mode under different working mode. A series of theoretically and practically valuable algorithms are proposed, and the good performances of them are verified by simulation experiments with real data. The main contributions of this dissertation are concluded as follows:
1. The analysis methods of the individual transmitter character under stable conditions and unstable conditions are studied systematically, and then the basic frame of individual transmitter identification is established. The definition of the individual transmitter character is given clearly, i.e. only the universal, unique, stable and
通信电台个体特征的分析、研究在电台个体识别中至关重要，本文主要针对相同工作模式、相同型号下的不同电台，研究通信电台个体识别中这个关键环节——电台个体特征分析。论文研究了个体特征的基本理论，并从其产生机理出发，研究了电台不同工作状态下个体特征的分析方法，包括电台稳定工作状态下的噪声特性、杂散特性和频率特性，以及非稳定工作状态下电台暂态特征分析，并在非稳定工作状态下探讨了不同工作模式下电台个体识别的解决方案。文中提出、推导了一系列具有理论及实用价值的新算法，并通过实际电台数据和计算机仿真实验验证了所提算法的优良性能。归纳起来，本文的贡献主要包括以下几个方面：
1、全文从电台稳定工作状态和非稳定工作状态系统地研究了电台的个体特征分析方法，建立了通信电台个体识别基本框架。文中明确给出了通信信号个体特征的定义，即只有具有普遍性、唯一性、稳定性和可检测性的信号特征方可作为识别电台个体的个体特征。同时，系统、全面地分析了个体特征产生机理，为个体特征分析提供了理论基础。为衡量所选特征的分类能力，提出了一种评价特征集分离性能的指标——可分离指数，根据该指标的测试，可以择优选相关特征作为电台的个体特征，为后续分析提供了一个分析工具。
2、针对稳定工作状态下硬件差异在信号上的不同体现，论文从噪声特性、杂散特性和频率三方面进行了个体特征分析研究。
在噪声特性分析方面，分析了各电台噪声统计特性上的差异，研究了噪声功率估计算法，提出了一种基于最小二乘法一特征值分解(LS-EVD)的噪声功率估计的改进算法，该算法不需要任何先验知识，在中等信噪比条件下噪声估计性良好，
Individual communication transmitter identification is an important issue in the field of communication reconnaissance in recent years. With the characteristics reflected on signal by the difference of the transmitter hardware, this issue focuses on seeking the source of the received signal. So the transmitter tracking can be realized and a significant thread can be provided for the determination of communication network construction. The study of this subject is different from the study of the traditional communication reconnaissance. Analyzing and intercepting the transmitted information are important subjects of the traditional communication reconnaissance but they are not the focuses of the individual transmitter identification, and the individual characteristics from the difference of the hardware will be removed or diminished as noise when studying the traditional communication reconnaissance but will be the emphasis when studying transmitter individual identification.
The individual transmitter character analysis is vital for communication transmitter identification. Aiming at the different transmitters of the same type and working mode, the individual transmitter character analysis is taken in this dissertation. With the basic theory and the mechanism, researching work is taken on the individual character analyzing method of different working mode, the stable working characters including the noise character, the spur character and the frequency character, and the transient character analysis of unstable working mode. A solution is proposed aiming at transmitter identification of different working mode under different working mode. A series of theoretically and practically valuable algorithms are proposed, and the good performances of them are verified by simulation experiments with real data. The main contributions of this dissertation are concluded as follows:
1. The analysis methods of the individual transmitter character under stable conditions and unstable conditions are studied systematically, and then the basic frame of individual transmitter identification is established. The definition of the individual transmitter character is given clearly, i.e. only the universal, unique, stable and
引文
[1] 穆世强，雷达信号脉内细微特征分析，电子对抗技术，1991，6(5)：28-37．
[2] 刘刚，雷达指纹分析的基本理论探讨，电子对抗，2002，6：1-6．
[3] 魏东升，雷达信号脉内细微特征分析的时频分析，电子对抗，1993，4：7-19．
[4] 牛海，马颖，小波—神经网络在辐射源识别中的应用研究，系统工程与电子技术，2002，124(15)：55-57．
[5] 张国柱，周一宇，姜文利，基于贝叶斯理论的辐射源分类识别方法研究，信号处理，2004，20(4)：350-352．
[6] McLaughlin, J., Droppo, J., Atlas, L., Class-dependent time-frequency distributions via operator theory, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-97., 1997, 3: 2045-2048.
[7] Bradford W. Gillespie, L. Atlas, optimizing time-frequency kernes for classification, IEEE Trans. on Siganl Processing, 2001, 49(3): 485-495.
[8] T. Hirano, T. Sugiyama, M. Shibuki, et al., Study on time-frequency spectrum pattern for radio transmitter identification, spring conf. of ieice, Mar. 2000, B-4-24: 121-127.
[9] Ralph D., Hippenstiel and Ycin Paya, Wavelet Based Transmitter Identification, ISSPA, Gold Coast, Australia, 1996, 740-743.
[10] R. D. Hippenstiel and Y. P. Wavelet based transmitter identification. In International Symposium on Signal Processing and its Applications, Gold Coast Australia, August 1996.
[11] David Wiselll and Tommy Oberg, Analysis and Identification of Transmitter Non-linearities, http://www.signal.uu.se/Publications/pdf/c0014.pdf
[12] N. Serinken and O. Ureten, Generalised dimension characterisation of radio transmitter turn-on transients, Electronics Letters, 2000, 36(12): 1064-1066.
[13] K. Iwaski, T. Hirano, M. Shibuki, et al., A data analysis method for radio transmitter identification based on transient response-root-music method, spring conf. of ieice, Mar. 1999, B-4-70: 25-31.
[14] K. Iwaski, T. Hirano, M. Shibuki, T. Sugiyama, Adaptive array algorithms for radio transmitter identification based on the trasient response, Trans. of ieice, B, Jul. 2001, J84-B(7): 1233-1238.
[15] J. Hall, M. Barbeau, E. Kranakis. Detection of transient in radio frequency fingerprinting using phase characteristics of signals. In L. Hesslink (Ed.), Proceedings of the 3rd International IASTED Conference on Wireless and Optical Communication, Banff, Canada, 2003, 13-18.
[16] 王伦文，钟子发，2FSK信号“指纹”特征的研究，电讯技术，2003，3：45-48．
[17] M. Turkboylari, G. L. Stuber, An efficient algorithm for estimating the signal-to-interference ratio in TDMA cellular systems, IEEE Trans Comm., 1998, 46(6): 728-731.
[18] D. R. Pauluzzi, N. C. Beaulieu, A comparison of SNR estimation techniques for the AWGN channel, IEEE Trans Comm., Oct., 2000, 48 (10): 1681-1691.
[19] D. -K., Hong, C. -H. Park, M. -C. Ju, et al, SNR Estimation in Frequency Domain, IEICE Trans. Comm., Mar. 2003, E86-B(3): 1174-1175.
[20] A. Ramesh, A. Chockalingam, L. B. Milstein, SNR Estimation in Nakagami-m Fading with Diversity Combining and Its Application to Turbo Decoding, IEEE Trans. Comm., Nov., 2002, 50(11): 1719-1724.
[21] A. Wiesel, J. Goldberg, H. Messer, Non-Data-Aided Signal-to-Noise-Ratio Estimation, ICC2002, 2002: 197-201.
[22] T. R. Benedict and T. T. Soong, The joint estimation of signal and noise from the sum envelope, IEEE Trans. Inform. Theory, July 1967, IT-13: 447-454.
[23] R. Matzner, An SNR estimation algorithm for complex baseband signals using higher order statistics, Facta Universitatis (Nis), 1993, (6): 41-52.
[24] M. Andersin, N. B. Mandayam, R. D. Yates, Subspace Based Estimation of the Signal to Interference Ratio for TDMA Cellular Systems, VTC'96, 1996, 1155-1159.
[25] M. Wax and T. Kailath, Dection of signals by Information Theoretic Criteria, IEEE Trans. ASSP, 1985, 33(2): 387-392.
[26] 范海波，陈军，曹志刚，AWGN信道中非恒包络信号SNR估计算法，电子学报，2002，30(9)：1369-1371．
[27] 詹亚峰，曹志刚，马正新，无线数字通信的盲信噪比估计，清华大学学报，2003，46(7)：957-960．
[28] F. F. Liedtke, Computer Simulation of An Automatic Classification Procedure for digital Modulation Communication Signals With Unknown Parameters, Signal Processing, 1984, 6: 311-323.
[29] L. V. Dominguez, J. M. Paez Borrallo, J. P. Garcia, "A General Approach to The Automatic Classification of Radio communication Signals", Signal Processing, 1991, 22: 239-250.
[30] M. P. Desimio, G. E. Prescott, "Adaptive Generation of Decision functions forclassification of digitally Modulation Signals", NAECON' 88, 1988, 1010-1014.
[31] 肖先赐，现代谱估计—原理与应用，哈尔滨工业大学出版社，1991．
[32] 张贤达，现代信号处理，北京：清华大学出版社，1995．
[33] Lamontagne, R., Modulation Recogntion: An Overview, NTIS No: N92-20159/9/HDM, Defence Research Establishment, Ottawa, Ontario, March 1991.
[34] H. Ketterer, f. Jondral, A. H. Costa, Classification of Modulation Modes Using Time-Frequency Methods, ICASSP'99, 1999, 2471-2474.
[35] W. A. Gardner, C. M. Spooner, Cyclic Spectral Analysis for Signal Detector and Modulation Recognition, Milcom' 88, 1988, 419-424.
[36] B. Seaman, R. M. Braun, Using Cyclostationarity in the Modulation Classification of Analogue Signals, COMSIG'98, 1998, 261-266.
[37] A. Swami, B. M. Sadler, Hierarchical Digital Modulation Classification Using Cumulants, IEEE Trans. Comm., Mar, 2000, 48(3): 416-429.
[38] S. -Z. Hsue, S. S. Soliman, Automatic Modulation Classification Using Zero Crossing, IEE proceedings, Dec 1990, 137(6): 459-464.
[39] C. Dubuc, D. Boudrear, F. Patenaude, et al, An Automatic Modulation Recognition Algorithm for Spectrum Monitoring Applications, ICC'99, Vancouver, June, 1999, 732-736.
[40] P. Bianchi, Ph. Loubaton, F. Sirven, Performance of a Non Data Aided Estimator of the Modulation index of Continuous Phase Modulations, ICASSP2002, 2002, pp. 2377-2380.
[41] G. T. Zhou, G. B. Giannakis, Parameters Estimation of FM Signals Using Cyclic Statistics, Proceedings of IEEE-SP International symposium on time-frequency and time-scale analysis, 1994, 248-251.
[42] J. E. Gaby, Automatic Bit Pattern Analysis of Communications Signals, ICASSP'90, 1990, 1715-1718.
[43] A. W. Wegener, Fractical Techniques for Baud Rate Estimation, ICASSP'92, 1992, 681-684.
[44] A. K. Nandi, E. E. Azzouz. Automatic analogue modulation recognition, Signal Processing, 1995, 46: 211-222.
[45] 刘渝，快速高精度正弦波频率估计综合算法，电子学报，1999，27(6)：126-128．
[46] Y. -C. Xiao, P. Wei, X. -C. Xiao and H. -M. TaiO, Fast and accurate single frequency estimator, Electronics Letters 8th, July 2004, 40(14): 910-911.
[47] 刘贵江，冯小平，一种适用于数字调制信号的载波频率估计方法，系统工程与电子技术，2004，26(1)：1787-1789．
[48] K. Abdellah, B. Khier, Instantaneous Frequency Estimation Using Two-Sided Linear Prediction, CCA'98, 1998, 2: 1245-1249.
[49] Lang, S. W., Musicus B. R., Frequency estimation from phase differences, ICASSP-89., May 1989, 4: 2140-2143.
[50] S. Umesh, D. Nelson, Computationally efficient estimation of sinusoidal frequency at low SNR, Proc. IEEE ICASSP., May 1996, 5: 2797-2800.
[51] S. D. Casey, b. M. Sadler, Modifications of the Euclidean algorithm for isolating periodicities from a sparse set of noisy measurements, Proc. IEEE ICASSP., 1996, 44(9): 2260-2272.
[52] B. M. Sadler, S. D. Casey, Frequency estimation via sparse zero crossings, Proc. IEEE ICASSP., May 1996, 5: 2990-2993.
[53] P. D. Fiore, S. W. Lang, efficient phase-only frequency estimation, Proc. IEEE ICASSP., May 1996, 5: 2809-2812.
[54] H. E. Jones, Weighting coefficients for chirp rate estimation, IEEE Trans. SP., Jan. 1995, 43(1): 366-367.
[55] D. K. im, M. J. Narasimbs and D. C. Cox, An improved single frequency estimator, Signal Processing Letters, IEEE, July 1996, 3(7): 212-214.
[56] S. Kay, Statistically/computationally efficient frequency estimation, Proc. IEEE ICASSP., April 1988, 4: 2292-2295.
[57] Steven Kay, A Fast and Accurate Single Frequency Estimator, IEEE Trans. on Acoustics. Speech and Signal Processing, 1989, 37(12): 1987-1990.
[58] C. S. Ramalingam, On the equivalence of DESA-la and Prony's method when the signal is a sinusoid, Signal Processing Letters, IEEE., May 1996, 3(5): 141-143.
[59] 张贤达，保铮，非平稳信号分析与处理，北京：国防工业出版社，1998．
[60] N. Delprat, Asymptotic Wavelete and Gabor Analysis: Extraction of Instantaneous Frequencies, IEEE Trans. on Information Theory. 1992, Vol IT-38(3): 644-664
[61] L. Cohen, Time-frequency Analysis, Englewood Cliffs, NJ: Prentice hall, 1995.
[62] Mark A. wickert0, The Effect of Multipath on th Detection of Symbol-Rate Spectral Lines by Delay and Multiply Receivers, IEEE Journal on Selected areas in Communications. 1992, 10(3): 545-549
[63] Jean Claude Imbeaux, Performances of the Delay-Line Multiplier Circuit for Clock and Carrier Synchronization in Digital Satellite Communications, IEEE Journal on Seltcted Areas in Communications, 1983, SAC-1(1): 82-92
[64] Mark A. wickert, L. randy, Tuecotte, Rate-Line Detection Using Higher-order Spectra, IEEE MILCOM '92, 1992, 3: 1221-1225.
[65] P. Ciblat, P. Loubaton, E. Sperpedin, and G. B. Giannalis, Asymptotic analysis of blind cylic correlation-based symbol-rate estimators, IEEE transaction on Information Theory, july 2002, 48(7): 1992-1934.
[66] Zaihe Yu, Yun Q. shi, Wei Su, Symblo-rate Estimation based on Filter Bank, ISCAS 2005, 2: 1437-1440.
[67] Bruce Potter, Wireless Intrusion Detection, www.itsec.gov.cn/webportal/download/88.pdf.
[68] Mohan K., Chirumamilla and B. Ramamurthy. Agent based intrusion detection and response system for wireless LANs. ICC'03, 2003, 1: 492-496.
[69] Michael J. Riezenman. Cellular security: better, but foes still lurk. IEEE Spectrum, June 2000, 39-42.
[70] Jeyanthi Hall, Michel Barbeau, and Evangelos Kranakis. Detection of transient in radio frequency fingerprinting using signal phase. In Wireless and Optical Communications, ACTA Press, July 2003, 13-18.
[71] 郭梯云，邬国扬，李建东，移动通信，西安电子科技大学出版社，2000．
[72] 张玉兴，射频摸拟电路，电子工业出版社，2001．
[73] B. Razavi, Analysis, modeling, and simulation of phase noise in monolithic voltage-controlled oscillators, in Proceedings of Custom Integrated Circuits Conference. IEEE. May 1995, 323-326.
[74] B. Razavi, A study of phase noise in CMOS oscillators, IEEE Journal of Solid-State Circuits, Mar. 1996, 31(3): 331-343,
[75] Leon W．Couch，数字与模拟通信系统，电子工业出版社，2003．
[76] 曹志刚，钱亚生，现代通信原理，清华大学出版社，1992．
[77] 张厥盛，郑继禹，万心平，锁相技术，西安电子科技大学出版社，2002．
[78] 万心平，通信工程中的锁相环路，西北电讯工程学院出版社，1980．
[79] 张贤达，保铮，通信信号处理，国防工业出版社，2002．
[80] Kohei Yamashita and Tetsuya Shimamura, Nonstationary Noise Estimation Using Low Frequency Regions for Spectral Subtraction, IEEE Signal Processing Letters, June 2005, 12(6): 465-467.
[81] Dae-ki Hong, Cheol-Hee Park etc., SNR estimation in frequency domain using circular correlation, Electronics Letters, 2002, 38(25): 1693-1694.
[82] R. Matzner, F. Englberger, An SNR Estimation Algorithm Using Fourth-Order Moments, Proceedings of the 1994 IEEE Symposium on Information Theory, Trodheim, 1994, 119.
[83] T A Summer, S G Wilson, SNR Mismatch and Online Estimation in Turbo Decoding, IEEE Trans. Communications, 1998, COM-46(4): 421-423.
[84] N. S. Alagha. Cramer-Rao Bounds of SNR Estimates for BPSK and QPSK Modulated Signals. IEEE Trans. On SP., Jan. 2001, 45: pp. 10-12.
[85] 肖明耀，误差理论与应用，北京：中国计量出版社，1985．
[86] 费业泰，误差理论与数据处理，北京：机械工业出版社，1995．
[87] 宗殿瑞，宋文臣，刘朋振，最小二乘法应用探讨，青岛化工学院学报，199819(5)：296～301．
[88] 黄振华等，模式识别原理，杭州：浙江大学出版社，1991．
[89] 黄德双，神经网络、模式识别系统理论，北京：电子工业出版社，1996．
[90] 边肇祺等，模式识别，北京：清华大学出版社，1988．
[91] 吕铁军，通信信号调制识别研究：[博士学位论文]，成都：电子科技大学，2000．
(92] 詹亚峰，通信信号自动制式识别及参数估计：[博士学位论文]，北京：清华大学，2004．
[93] 王碧泉，陈祖荫，模式识别，北京：地震出版社，1989．
[94] 文贡坚，基于模糊决策的快速识别多类目标的方法，模式识别与人工智能，1997，10(2)：107～111．
[95] T. Chen, the past, present, and future of neural networks for signal processing, IEEE signal processing magazine, November 1997.
[96] 杨行峻，郑君里，人工神经网络，北京：高等教育出版社，1992．
[97] 俞瑞钊等，人工智能原理与技术，杭州：浙江大学出版社，1993．
[98] A. K. Nandi, E. E. Azzouz, Modulation recognition using artificial neural networks, Signal processing, 1997, 56: 165-175.
[99] 陈国良等，人工神经网络理论研究进展，电子学报，1996，24(1)：70-75．
[100] 胡守仁，神经网络应用技术，长沙：国防科学技术大学出版社，1993．
[101] N. Ueda, Optimal linear combination of neural networks for improving classification performance, IEEE Trans. Pattern analysis and machine intelligence, 2000, 22(2): 207-215.
[102] M. Delgado, F. Herrera, E. H. Viedma, L. Martinez, Combining numerical and linguistic information in group decision making, Journal of information sciences, 1998, 107: 177-194.
[103] W. G. Baxt, Improving the accuracy of an artificial neural network using multiple differently trained networks, Neural computation, 1992, 4: 772-780,
[104] A. Hunter. Feature Selection using Probabilistic Neural Networks. Neural Computing and Applications. 2000, 124-132.
[105] W. Kinsner, V. Cheung, K. Cannons, Signal Classification through Multifractal Anaysis and Complex Domain Neural Networks. IEEE ICCI'03, Aug. 2003, 41-46.
[106] Zaknich, A., deSilva, C. J. S., A Modified Probablistic Neural Network (PNN) for Noninear Time Series Analysis. IEEE. International Joint Conference on Neural Networks, 1991, 2: 1530-1535.
[107] Huang Deshuang, Ma Songde, a New Radial Basis Probabistic Neural Network Model, ICSP'96, 1996, 1449-1452.
[108] A. Hunter. Feature Selection using Probabilistic Neural Networks. Neural Computing and Applications. 2000, 124-132.
[109] J. Toonstra and W. Kinsner. Transient Analysis and Genetic Algorithms for Classification. IEEE WESCANEX 95. 1995, 432-437.
[110] M. Srinivas, L. M. Paynaik, Genetic algorithms: A survey, IEEE computer, June 1994, 17-26.
[111] 蔡权伟，多分量信号的信号分量分离技术研究：[博士学位论文]，电子科技大学，2006．
[112] F. Jondral, Automatic classification of high frequency signals, Signal Processing, October 1985, 9(3): 177-190.
[113] Gardner. W, Spectral correlation of modulated signals: PART I-Analogue Modulation, IEEE Trans. on Comm., 1987, 35(6): 584-594.
[114] Gardner. W, Spectral correlation of modulated signals: PART I-Digital Modulation, IEEE Trans. on Comm., 1987, 35(6): 595-601.
[115] Y. T. Chan, L. G. Gadbois, Identification of the modulation type of a signal, Signal Processing, 1989, 16: 149-154,
[116] S. Taira, E. Murakami, Automatic classification analogue modulation signals by statistical parameters, IEEE, MILCOM, Nov. 1999, 1: 202-207.
[117] Xin Ou; Xiaowei Huang; Quasi-Haar wavelet and modulation identification of digital signals. ICCCAS 2004. Communications, Circuits and Systems, June 2004, 2: 733-737.
[118] Swamia, Sadler B M. Hierarchical, Digital Modulation Using Cumulants. IEEE Trans. Communications, 2000, 48(3): 416-429.
[119] ZHAN Ya-feng, CAO Zhi-gang, MA Zheng-xin. Modulation classification of M-QAM signals. JOURNAL OF CHINA INSTITUTE OF COMMUNICATIONS. 2004, 25(2): 68-74.
[120] LI Chao, KUO Yong-hong, Qam signals recognition based on fractal research of constellation. Information Technoloyg, 2005, 3: 23-25.
[121] Drnmright, T. A.; Zhi Ding, A new algorithm for QAM signal classification in AWGN channels, IEEE International Symposium on, May 2002, 1: Ⅰ-849-Ⅰ-852
[122] Dobre, O. A.; Bar-Ness, Y.; Wei Su; Higher-order cyclic cumulants for high order modulation classification, MILCOM 2003. IEEE, 1: 112-117.
[123] Hadinejad-Mahram, H.; Hero, A. O., III; Robust QAM modulation classification via moment matrices, PIMRC 2000. The 11th IEEE International Symposium on, 1: 133-137.
[124] Soo-Chang Pei, etc. High resolution Wigner Distribution using Chirp Z-Transform analysis, Signal Processing, 1990, 161-163.
[125] 崔锦泰，小波分析导论，西安交通大学出版社，1995．
[126] Li-Chen Lin, et al. c. On the convergence of wavelet-based iterative signal extrapolation algorithms, Signal Processing, 1996, 48: 51-65.
[127] Daubechies L. The Wavelet Transform Time-Frequency Localization and Sign Analysis, IEEE Trans on Information Theory, 1990, 36(5): 961-1006.
[128] L. Cohen. Time-Frequency Distribution-A Review, Proceeding of The IEE, July 1989, 77(7): 941-981.
[129] 纪跃波，秦树人，汤宝平，Winger分布干扰项抑制及其算法．重庆大学学报(自科版)，2001，24(4)：26-30．
[130] 陈端，刘树棠，基于离散Gabor变换的抑制交叉项的新方法，西安交通大学学报，1997，31(9)：77-80．
[131] Zhang Haiyong, Ma Xiaojiang, GaiQiang. Wigner-Ville Distribution Based on Intrinsic Mode Function. 2001. Radar, 2001 CIE International Conference on, Proceedings 15-18 Oct. 2001, 1015-1017.
[132] Gai Qiang, Ma Xiaojiang, Zhang Haiyong, et al. Processing Time-Varying Signals by a New Method. Radar, 2001 CIE International Conference on, Proceedings 15-18 Oct. 2001, 1011-1014.
[133] Richaro G Baraniuk, Douglas L Jones. Signal-dependent Time-frequency Representation. Signal Processing, 1993, 32: 263-284.
[134] 王衍文，余鹏，程敬之，心脏杂音的自适应时频谱分析，生物物理学报，1999，15(2)：351-359．
[135] 陈光化，马世伟，曹家麟，基于分数阶傅立叶变换的自适应时频表示，系统工程与电子技术，2001，23(4)：69-71．
[136] 陈光化，曹家麟，王健，等，应用自适应时频分布的瞬时频率估计，系统工程与电子技术，2002，24(1)：31-34．
[137] Mallat S. G., Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on signal processing, 1993, 41(12): 3397-3415.
[138] Shie Qian, Dapang Chen. Signal Representation using adaptive normalized Gaussian function. Signal Preceessing, 1994, 36: 1-11.
[139] Norden E. Huang, Zheng Shen, Steven R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysi. Proc. R. Soc. Lond. A, 1998, 454: 903-995.
[140] Lob CH, et al. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2001, 91(5): 1339-1357.
[141] Echeverria J. C., et al. Application of empirical mode decomposition to heart rate variability analysis[J]. Medical & Biological Engineering & Computing, 2001, 39: 471-479.
[142] Ivan Magrin-Chagnolleon, Richard G Empirical mode decomposition based time-frequency attributes. Proc. of the 69th SEG Meeting, 1999, 41-60.
[143] Huang N. E, Shen Z, Long SR, et al. A new method for nonlinear and nonstationary time series analysis. 4th international conference on stochastic structural dynamics, 1999, 559-564.
[144] Huang N. E., Review of Empirical Mode Decomposition. Proc. of SPIE, 2001, 4391: 71-79.
[145] Huang N. E., A New method for nonlinear and nonstationary time series analysis: Empirical mode decomposition and Hilbert spectral analysis. Proc. of SPIE, 2000, 4056: 197-209.
[146] Timashev SA, Shalin MG. Precise Analysis of Non-stationary Vibration Processes Using the Hilbert Transform. ACSIM 2000 Proceeding, 2000, 395-404.
[147] Wang N., Empirical mode decomposition and acoustic inversion of discrete layered media. Acoustics Letters, 2001, 23(7): 145-148.
[148] Norden E. Huang, Z. S., Stever R. Long, et al., A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
[149] T.Schlurmann. Spectral Frequency Analysis of Nonlinear Water Waves Based on the Hilbert-Huang Transformation. Proceeding of OMAE'01,20th international Conference on Offshore Mechanics and Arctic Engineering, 411-418.
[150] Komm RW, H. F., Empirical mode decomposition and Hilbert analysis applied to rotation residuals of the solar convection zone. Astrophysical Journal, 2001, 559(1):428-441.
[151] Dionisio Bernal, Burcu Gunes, An Examination of Instantaneous Frequency as a Damage Detection Tool. Department of Civil and Environmental Engineering, 427 Spell Engineering Center, Northeastern University, Boston, MA 02115, U. S. A.
[152] H. T. Vincent, S. L.H., Z.Hou, Damage Detection Using Empitical Mode Decomposition Method and a Comparison with Wavelet Analysis. Proc. of the 2nd international workshop on structural health monitoring, 2000.
[153] Yang Jann N., L. Y., System identification of linear structures using Hilbert transform and Empirical Mode Decomposition. Proceedings of the International Modal Analysis Conference, 2000,213-219.
[154] Kang Huang, Dave Tateh Chang, Thomas Hou. A Bridge Monitoring Method Based on Vibration Characteristics Under a Transient Load. Proceeding of the International Symposium of Civil Engineering in the 21th Century, Beijing, China, 2000, 563-565.
[155] Gravi er B. M., N. N. J., Pelstring .T. A., An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers. Proceedings of the International Offshore Yue Huanyin> G. H, Han Chunming, Li Xinwu, et al, A SAR Interferogram Filter Based on the Empirical Mode Decomposition Method. Geoscience and Remote Sensing Symposium, ICARSS'01. IEEE 2001 International, 2001, 5:2061-2063.
[156] 邓拥军,王伟,钱成春, EMD 方法及Hilbert 变换中边界问题的处理。科学通报,2001, 46 (3): 257-263.
[157] O. Bessen, P. Stoica, Frequency estimation and detection for sinusoidal signals with arbitrary envelope: a nonlinear least-squares approach, Proc. IEEE ICASSP-98, 1998, 4:2209-2212.
[158] B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals, Proc.IEEE, April 1992, 80(4):520-538.
[159] B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications, Proc.IEEE, April 1992, 80(4):540-568.
[160] R.J. Mammone, R.J. Rothaker, C.I. Podilchuk, Estimation of Carrier Frequency, Modulation Type and Bit Rate of An Unknown Modulated Signal, ICC'87, 1987, 1006-1012.
[161] LiangHong, K. C. Ho, Identification of Digital Modulation Type Using The Wavelete Transform, IEEE MILCOM 1999, 1: 427-431.
[162] Shenna M. The Discrete Wavelete Transform: Wedding the A-irous and Mallat Algorithm, IEEE Trans. On SP, 1992, SP-40(10): 2464-2482.
[163] 何哲平，肖先赐，PSK信号检测的小波变换方法，中国电子学会电子对抗第五届学术年会论文集，1997年10月，成都，684-687．
[164] K. C. Ho, W. Prokopiw, et al, Modulation Identification of Digital Signal by the Wavelet Transform, IEE Proc. Radar, Sonar Navig, 2000, 147(4): 169-176.
[165] Hermann Roh Ling, Radar CFAR Thresholding in Clutter and Multiple Target Situation, IEEE Trans. on AES, 1983, AES-19(47): 608-620.
[166] Masaaki SHIBUKI, Tsutomu SUGIYAMA, Ken Iwasaki, Transmitter Identification—The Development of a High speed Data Acquisition System with Receiving Functions, Journal of the Communications Research Laboratory, 2002, 49(1): 129-135.
[167] D. Fox etal, Bayesian Filtering for location estimation, Pervasive Computing. 2003, 24-33.
[168] S. J. Russell and P. Norvig. Artificial Intelligence: Modem Approach. Prentice Hall, 2002.
[169] Camastra F, Vinciareli A. Intrinsic dimension estimation of data: An approach based on Grassber-Procaccia's algorithm. Neural Processing Letters, 2001, 14(1): 27-34.
[170] 蒋东翔，黄文虎，徐世昌，分形几何及其在故障诊断中的应用，哈尔滨工业大学学报，1996，28(2)：27～31．
[171] 张文明，李莉，申焱华，滚动轴承故障诊断中的分形．北京科技大学学报，1996，18(3)：215～219．
[172] Zhang G X, Jin w D，Hu L z. Radar Emitter Signal Recognition Based on Complexity Features. Journal of Sounthwest Jiaotong University, 2004, 12(2): 116-122.
[173] Montesino Otero M E Rolo Naranjo A, Aoy Carralero A. The attractor dimension determination applied to monitoring and surveillance in nuclear power plants. Progress in Nucear Energy, 2003, 43(1): 389-395.
[174] Logan D., Mathew J., Using the correlation dimension for vibration fault diagnosis of roll ingelement bearing-basic concepts. Mechanical Systems and Signal Processing, 1996, 10(3): 241-250.
[175] 吕铁军，魏平，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．
[176] Takers F. Detecting strange attractor in turbulence, in Dynamical systems and turbulence, Warwich, 1980 Lecture Notes in Mathematics, Vol. 898, Rand and Young eds, 1981: 366-381.
[177] H Whitney. Theself-intersections of a smoothn-manifold in 2n-space. AnnMath, 1994, 45: 220-246.
[178] D. R. J. Chilling worth. Differential Topology with a View to Applications. Loton: Pitman, 1976.
[179] Logan D., Mathew J., Using the correlation dimension for vibration fault diagnosis of roll ingelement bearing-basic concepts[J]. Mechanical Systems and Signal Processing, 1996, 10(3): 241～250.
[180] 姜建东，屈梁生，相关维数在大机组故障诊断中的应用，西安交通大学学报，1998，32(4)：27～31．
[181] Grassberger E, Procaccia I. Characterization of strange attractors. Phys. Rev. Lett. A, 1983, 50(5): 346～349.
[182] 汪慰军，陈进，吴昭同，等，关联维数的计算及其在大机组故障诊断中的应用，上海交通大学学报，2000，34(9)：1265-1268．
[183] 谢和平，薛秀谦，分形应用中的数学基础与方法，北京：科学出版社，1997．
[184] Takers F. Detecting strange attractor in turbulence, in Dynamical systems and turbulence, Warwich, 1980 Lecture Notes in Mathematics, Vol. 898, Rand and Young eds, 1981, 366-381.
[185] H Whitney. The self-intersections of a smooth n-manifold in 2n-space. Ann Math, 1994, 45: 220-246.
[186] 林梓，李魁俊，王丽，等，混沌吸引子的重构，长春邮电学院学报，1999，17(3)：7-12．
[187] Beylkin G., Cofiman R., Rokhlin V., Fast Wavelet Transform and Numberical Algorithms Ⅰ, Comm. On Pure and Appl. Math 1991, 44(2): 141-183.
[188] 王祖林，周萌清，多重分形谱及其计算，北京航空航天大学学报，2000，26(3)：256-258．
[189] L. Sun and W. Kinsner, Characterization and Feature Extraction of Transient Signals Using Multifractal Measures, IEEE Canadian Conference on Electrical and Computer Engineering, May 1999, 2: 781-785.
[190] Yakov Lantsman and John A., Actuarial Application of Multifractal Modeling, 2001 Winter Forum.
[191] 杜干，张守宏，基于多重分形的雷达目标的模糊检测，自动化学报，2001，27(2)：174-179．
[192] J. Chen and W. Kinsner, Multifractal Analysis of Transients in Power Systems, Electrical and Computer Engineering, 2000 Canadian Conference on, 2000, 1: 307-311.
[193] A. K. Nandi, E. E. Azzouz, Modulation recognition using artificial neural networks, Signal processing, 1997, 56: 165-175.
[194] Q. Jiang, S. S. Goh, Z. Lin, Local discriminant time-frequency atoms for signal classfication, Signal processing, 1999, 72:47-52.
[195] L. V. Dominguez, J. M. P. Borrallo, J. P. Garcia, A general approach to the automatic classification of radiocommunication signals, Signal processing, 1991, 22:239-250.
[196] Yawpo yang, Ching-hwa liu, Ta-wei soong, A log-likelihood function-based algorithm for QAM signal classification, Signal processing, 1998, 70:61-71.
[197] D.E.Eckhardt, JR. and L.D.Lee, A theoretical basis for the analysis of multiversion software subject to coincident errors, IEEE Trans, on Software Engineering, Dec 1985, SE-11(12):1511-1517.
[198] D. Fox et al. Bayesian Filtering for location estimation. Pervasive Computing. 2003, 24-33.
[199] J. Toonstra and W. Kinsner. Transient Analysis and Genetic Algorithms for Classification. IEEE WESCANEX 95, 1995, 432-437.
[200] 张葛祥,李娜,金炜东,等,一种新量子遗传算法及其应用,电子学报, 2004,32(3): 476-479.
[201] Li B, Zhuang Z Q, Genetic Algorithm Based-on the Quantum Probability Representation, Lecture Notes in Computer Science, 2002, 2412:500-506.
[202] X.M. Huo, D. Donoho, A Simple and Robust Modulation Classification Method Via counting, ICASSP'98, 1998, 3289-3292.
[203] V.J. Stolpman, S. Paranjpe, G. C. Orsak, A Blind Information Theoretic Approach to Automatic Signal Classification, Milcom'99, 1999, 447-451.
[204] Shi D, Shu W H,Liu H T. Feature seection for handwritten Chinese character recognition based on genetic agorithms. Proceedings of IEEE International Conference on System, Man, and Cybernetics. 1998, 4201-4206.
[205] Mitra P, Murthy C A, Pal S k. Unsuoervised feature selection using feature similarity. IEEE Transacions on Pattern Anaysis and Machine Intelligence, 2002, 24(3):301-312.
[206] Molina L. C, Belanche L., Nebot A., Feature selection algorithms: a survey and ecperimenta evauation. Proceedings of Internationa Conference on Data Mining, 2002, 306-313.
[207] Bressan M., Vitria J., On the selection and classification of independent features, IEEE Trans. on Pattern Anaysis and Machine Intelligence, 2003, 25(10):1312-1317.
[208] Guo G. D., Dyer C. R., Simultaneous selection and cassifier training via linear programming: a case study for face expression recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 346-352.
[209] 吕铁军，王河，肖先赐，新特征选择方法下的信号调制识别，电子与信息学报，2002，24(5)：661-666．
[210] Zhang G. X., Hu L. Z., Jin W. D., Resembance coefficient and a quantum genetic agorithm for feature selection. Lecture Notes in Artificial Intelligence, 2004, 3245: 155-168.
[2] 刘刚，雷达指纹分析的基本理论探讨，电子对抗，2002，6：1-6．
[3] 魏东升，雷达信号脉内细微特征分析的时频分析，电子对抗，1993，4：7-19．
[4] 牛海，马颖，小波—神经网络在辐射源识别中的应用研究，系统工程与电子技术，2002，124(15)：55-57．
[5] 张国柱，周一宇，姜文利，基于贝叶斯理论的辐射源分类识别方法研究，信号处理，2004，20(4)：350-352．
[6] McLaughlin, J., Droppo, J., Atlas, L., Class-dependent time-frequency distributions via operator theory, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-97., 1997, 3: 2045-2048.
[7] Bradford W. Gillespie, L. Atlas, optimizing time-frequency kernes for classification, IEEE Trans. on Siganl Processing, 2001, 49(3): 485-495.
[8] T. Hirano, T. Sugiyama, M. Shibuki, et al., Study on time-frequency spectrum pattern for radio transmitter identification, spring conf. of ieice, Mar. 2000, B-4-24: 121-127.
[9] Ralph D., Hippenstiel and Ycin Paya, Wavelet Based Transmitter Identification, ISSPA, Gold Coast, Australia, 1996, 740-743.
[10] R. D. Hippenstiel and Y. P. Wavelet based transmitter identification. In International Symposium on Signal Processing and its Applications, Gold Coast Australia, August 1996.
[11] David Wiselll and Tommy Oberg, Analysis and Identification of Transmitter Non-linearities, http://www.signal.uu.se/Publications/pdf/c0014.pdf
[12] N. Serinken and O. Ureten, Generalised dimension characterisation of radio transmitter turn-on transients, Electronics Letters, 2000, 36(12): 1064-1066.
[13] K. Iwaski, T. Hirano, M. Shibuki, et al., A data analysis method for radio transmitter identification based on transient response-root-music method, spring conf. of ieice, Mar. 1999, B-4-70: 25-31.
[14] K. Iwaski, T. Hirano, M. Shibuki, T. Sugiyama, Adaptive array algorithms for radio transmitter identification based on the trasient response, Trans. of ieice, B, Jul. 2001, J84-B(7): 1233-1238.
[15] J. Hall, M. Barbeau, E. Kranakis. Detection of transient in radio frequency fingerprinting using phase characteristics of signals. In L. Hesslink (Ed.), Proceedings of the 3rd International IASTED Conference on Wireless and Optical Communication, Banff, Canada, 2003, 13-18.
[16] 王伦文，钟子发，2FSK信号“指纹”特征的研究，电讯技术，2003，3：45-48．
[17] M. Turkboylari, G. L. Stuber, An efficient algorithm for estimating the signal-to-interference ratio in TDMA cellular systems, IEEE Trans Comm., 1998, 46(6): 728-731.
[18] D. R. Pauluzzi, N. C. Beaulieu, A comparison of SNR estimation techniques for the AWGN channel, IEEE Trans Comm., Oct., 2000, 48 (10): 1681-1691.
[19] D. -K., Hong, C. -H. Park, M. -C. Ju, et al, SNR Estimation in Frequency Domain, IEICE Trans. Comm., Mar. 2003, E86-B(3): 1174-1175.
[20] A. Ramesh, A. Chockalingam, L. B. Milstein, SNR Estimation in Nakagami-m Fading with Diversity Combining and Its Application to Turbo Decoding, IEEE Trans. Comm., Nov., 2002, 50(11): 1719-1724.
[21] A. Wiesel, J. Goldberg, H. Messer, Non-Data-Aided Signal-to-Noise-Ratio Estimation, ICC2002, 2002: 197-201.
[22] T. R. Benedict and T. T. Soong, The joint estimation of signal and noise from the sum envelope, IEEE Trans. Inform. Theory, July 1967, IT-13: 447-454.
[23] R. Matzner, An SNR estimation algorithm for complex baseband signals using higher order statistics, Facta Universitatis (Nis), 1993, (6): 41-52.
[24] M. Andersin, N. B. Mandayam, R. D. Yates, Subspace Based Estimation of the Signal to Interference Ratio for TDMA Cellular Systems, VTC'96, 1996, 1155-1159.
[25] M. Wax and T. Kailath, Dection of signals by Information Theoretic Criteria, IEEE Trans. ASSP, 1985, 33(2): 387-392.
[26] 范海波，陈军，曹志刚，AWGN信道中非恒包络信号SNR估计算法，电子学报，2002，30(9)：1369-1371．
[27] 詹亚峰，曹志刚，马正新，无线数字通信的盲信噪比估计，清华大学学报，2003，46(7)：957-960．
[28] F. F. Liedtke, Computer Simulation of An Automatic Classification Procedure for digital Modulation Communication Signals With Unknown Parameters, Signal Processing, 1984, 6: 311-323.
[29] L. V. Dominguez, J. M. Paez Borrallo, J. P. Garcia, "A General Approach to The Automatic Classification of Radio communication Signals", Signal Processing, 1991, 22: 239-250.
[30] M. P. Desimio, G. E. Prescott, "Adaptive Generation of Decision functions forclassification of digitally Modulation Signals", NAECON' 88, 1988, 1010-1014.
[31] 肖先赐，现代谱估计—原理与应用，哈尔滨工业大学出版社，1991．
[32] 张贤达，现代信号处理，北京：清华大学出版社，1995．
[33] Lamontagne, R., Modulation Recogntion: An Overview, NTIS No: N92-20159/9/HDM, Defence Research Establishment, Ottawa, Ontario, March 1991.
[34] H. Ketterer, f. Jondral, A. H. Costa, Classification of Modulation Modes Using Time-Frequency Methods, ICASSP'99, 1999, 2471-2474.
[35] W. A. Gardner, C. M. Spooner, Cyclic Spectral Analysis for Signal Detector and Modulation Recognition, Milcom' 88, 1988, 419-424.
[36] B. Seaman, R. M. Braun, Using Cyclostationarity in the Modulation Classification of Analogue Signals, COMSIG'98, 1998, 261-266.
[37] A. Swami, B. M. Sadler, Hierarchical Digital Modulation Classification Using Cumulants, IEEE Trans. Comm., Mar, 2000, 48(3): 416-429.
[38] S. -Z. Hsue, S. S. Soliman, Automatic Modulation Classification Using Zero Crossing, IEE proceedings, Dec 1990, 137(6): 459-464.
[39] C. Dubuc, D. Boudrear, F. Patenaude, et al, An Automatic Modulation Recognition Algorithm for Spectrum Monitoring Applications, ICC'99, Vancouver, June, 1999, 732-736.
[40] P. Bianchi, Ph. Loubaton, F. Sirven, Performance of a Non Data Aided Estimator of the Modulation index of Continuous Phase Modulations, ICASSP2002, 2002, pp. 2377-2380.
[41] G. T. Zhou, G. B. Giannakis, Parameters Estimation of FM Signals Using Cyclic Statistics, Proceedings of IEEE-SP International symposium on time-frequency and time-scale analysis, 1994, 248-251.
[42] J. E. Gaby, Automatic Bit Pattern Analysis of Communications Signals, ICASSP'90, 1990, 1715-1718.
[43] A. W. Wegener, Fractical Techniques for Baud Rate Estimation, ICASSP'92, 1992, 681-684.
[44] A. K. Nandi, E. E. Azzouz. Automatic analogue modulation recognition, Signal Processing, 1995, 46: 211-222.
[45] 刘渝，快速高精度正弦波频率估计综合算法，电子学报，1999，27(6)：126-128．
[46] Y. -C. Xiao, P. Wei, X. -C. Xiao and H. -M. TaiO, Fast and accurate single frequency estimator, Electronics Letters 8th, July 2004, 40(14): 910-911.
[47] 刘贵江，冯小平，一种适用于数字调制信号的载波频率估计方法，系统工程与电子技术，2004，26(1)：1787-1789．
[48] K. Abdellah, B. Khier, Instantaneous Frequency Estimation Using Two-Sided Linear Prediction, CCA'98, 1998, 2: 1245-1249.
[49] Lang, S. W., Musicus B. R., Frequency estimation from phase differences, ICASSP-89., May 1989, 4: 2140-2143.
[50] S. Umesh, D. Nelson, Computationally efficient estimation of sinusoidal frequency at low SNR, Proc. IEEE ICASSP., May 1996, 5: 2797-2800.
[51] S. D. Casey, b. M. Sadler, Modifications of the Euclidean algorithm for isolating periodicities from a sparse set of noisy measurements, Proc. IEEE ICASSP., 1996, 44(9): 2260-2272.
[52] B. M. Sadler, S. D. Casey, Frequency estimation via sparse zero crossings, Proc. IEEE ICASSP., May 1996, 5: 2990-2993.
[53] P. D. Fiore, S. W. Lang, efficient phase-only frequency estimation, Proc. IEEE ICASSP., May 1996, 5: 2809-2812.
[54] H. E. Jones, Weighting coefficients for chirp rate estimation, IEEE Trans. SP., Jan. 1995, 43(1): 366-367.
[55] D. K. im, M. J. Narasimbs and D. C. Cox, An improved single frequency estimator, Signal Processing Letters, IEEE, July 1996, 3(7): 212-214.
[56] S. Kay, Statistically/computationally efficient frequency estimation, Proc. IEEE ICASSP., April 1988, 4: 2292-2295.
[57] Steven Kay, A Fast and Accurate Single Frequency Estimator, IEEE Trans. on Acoustics. Speech and Signal Processing, 1989, 37(12): 1987-1990.
[58] C. S. Ramalingam, On the equivalence of DESA-la and Prony's method when the signal is a sinusoid, Signal Processing Letters, IEEE., May 1996, 3(5): 141-143.
[59] 张贤达，保铮，非平稳信号分析与处理，北京：国防工业出版社，1998．
[60] N. Delprat, Asymptotic Wavelete and Gabor Analysis: Extraction of Instantaneous Frequencies, IEEE Trans. on Information Theory. 1992, Vol IT-38(3): 644-664
[61] L. Cohen, Time-frequency Analysis, Englewood Cliffs, NJ: Prentice hall, 1995.
[62] Mark A. wickert0, The Effect of Multipath on th Detection of Symbol-Rate Spectral Lines by Delay and Multiply Receivers, IEEE Journal on Selected areas in Communications. 1992, 10(3): 545-549
[63] Jean Claude Imbeaux, Performances of the Delay-Line Multiplier Circuit for Clock and Carrier Synchronization in Digital Satellite Communications, IEEE Journal on Seltcted Areas in Communications, 1983, SAC-1(1): 82-92
[64] Mark A. wickert, L. randy, Tuecotte, Rate-Line Detection Using Higher-order Spectra, IEEE MILCOM '92, 1992, 3: 1221-1225.
[65] P. Ciblat, P. Loubaton, E. Sperpedin, and G. B. Giannalis, Asymptotic analysis of blind cylic correlation-based symbol-rate estimators, IEEE transaction on Information Theory, july 2002, 48(7): 1992-1934.
[66] Zaihe Yu, Yun Q. shi, Wei Su, Symblo-rate Estimation based on Filter Bank, ISCAS 2005, 2: 1437-1440.
[67] Bruce Potter, Wireless Intrusion Detection, www.itsec.gov.cn/webportal/download/88.pdf.
[68] Mohan K., Chirumamilla and B. Ramamurthy. Agent based intrusion detection and response system for wireless LANs. ICC'03, 2003, 1: 492-496.
[69] Michael J. Riezenman. Cellular security: better, but foes still lurk. IEEE Spectrum, June 2000, 39-42.
[70] Jeyanthi Hall, Michel Barbeau, and Evangelos Kranakis. Detection of transient in radio frequency fingerprinting using signal phase. In Wireless and Optical Communications, ACTA Press, July 2003, 13-18.
[71] 郭梯云，邬国扬，李建东，移动通信，西安电子科技大学出版社，2000．
[72] 张玉兴，射频摸拟电路，电子工业出版社，2001．
[73] B. Razavi, Analysis, modeling, and simulation of phase noise in monolithic voltage-controlled oscillators, in Proceedings of Custom Integrated Circuits Conference. IEEE. May 1995, 323-326.
[74] B. Razavi, A study of phase noise in CMOS oscillators, IEEE Journal of Solid-State Circuits, Mar. 1996, 31(3): 331-343,
[75] Leon W．Couch，数字与模拟通信系统，电子工业出版社，2003．
[76] 曹志刚，钱亚生，现代通信原理，清华大学出版社，1992．
[77] 张厥盛，郑继禹，万心平，锁相技术，西安电子科技大学出版社，2002．
[78] 万心平，通信工程中的锁相环路，西北电讯工程学院出版社，1980．
[79] 张贤达，保铮，通信信号处理，国防工业出版社，2002．
[80] Kohei Yamashita and Tetsuya Shimamura, Nonstationary Noise Estimation Using Low Frequency Regions for Spectral Subtraction, IEEE Signal Processing Letters, June 2005, 12(6): 465-467.
[81] Dae-ki Hong, Cheol-Hee Park etc., SNR estimation in frequency domain using circular correlation, Electronics Letters, 2002, 38(25): 1693-1694.
[82] R. Matzner, F. Englberger, An SNR Estimation Algorithm Using Fourth-Order Moments, Proceedings of the 1994 IEEE Symposium on Information Theory, Trodheim, 1994, 119.
[83] T A Summer, S G Wilson, SNR Mismatch and Online Estimation in Turbo Decoding, IEEE Trans. Communications, 1998, COM-46(4): 421-423.
[84] N. S. Alagha. Cramer-Rao Bounds of SNR Estimates for BPSK and QPSK Modulated Signals. IEEE Trans. On SP., Jan. 2001, 45: pp. 10-12.
[85] 肖明耀，误差理论与应用，北京：中国计量出版社，1985．
[86] 费业泰，误差理论与数据处理，北京：机械工业出版社，1995．
[87] 宗殿瑞，宋文臣，刘朋振，最小二乘法应用探讨，青岛化工学院学报，199819(5)：296～301．
[88] 黄振华等，模式识别原理，杭州：浙江大学出版社，1991．
[89] 黄德双，神经网络、模式识别系统理论，北京：电子工业出版社，1996．
[90] 边肇祺等，模式识别，北京：清华大学出版社，1988．
[91] 吕铁军，通信信号调制识别研究：[博士学位论文]，成都：电子科技大学，2000．
(92] 詹亚峰，通信信号自动制式识别及参数估计：[博士学位论文]，北京：清华大学，2004．
[93] 王碧泉，陈祖荫，模式识别，北京：地震出版社，1989．
[94] 文贡坚，基于模糊决策的快速识别多类目标的方法，模式识别与人工智能，1997，10(2)：107～111．
[95] T. Chen, the past, present, and future of neural networks for signal processing, IEEE signal processing magazine, November 1997.
[96] 杨行峻，郑君里，人工神经网络，北京：高等教育出版社，1992．
[97] 俞瑞钊等，人工智能原理与技术，杭州：浙江大学出版社，1993．
[98] A. K. Nandi, E. E. Azzouz, Modulation recognition using artificial neural networks, Signal processing, 1997, 56: 165-175.
[99] 陈国良等，人工神经网络理论研究进展，电子学报，1996，24(1)：70-75．
[100] 胡守仁，神经网络应用技术，长沙：国防科学技术大学出版社，1993．
[101] N. Ueda, Optimal linear combination of neural networks for improving classification performance, IEEE Trans. Pattern analysis and machine intelligence, 2000, 22(2): 207-215.
[102] M. Delgado, F. Herrera, E. H. Viedma, L. Martinez, Combining numerical and linguistic information in group decision making, Journal of information sciences, 1998, 107: 177-194.
[103] W. G. Baxt, Improving the accuracy of an artificial neural network using multiple differently trained networks, Neural computation, 1992, 4: 772-780,
[104] A. Hunter. Feature Selection using Probabilistic Neural Networks. Neural Computing and Applications. 2000, 124-132.
[105] W. Kinsner, V. Cheung, K. Cannons, Signal Classification through Multifractal Anaysis and Complex Domain Neural Networks. IEEE ICCI'03, Aug. 2003, 41-46.
[106] Zaknich, A., deSilva, C. J. S., A Modified Probablistic Neural Network (PNN) for Noninear Time Series Analysis. IEEE. International Joint Conference on Neural Networks, 1991, 2: 1530-1535.
[107] Huang Deshuang, Ma Songde, a New Radial Basis Probabistic Neural Network Model, ICSP'96, 1996, 1449-1452.
[108] A. Hunter. Feature Selection using Probabilistic Neural Networks. Neural Computing and Applications. 2000, 124-132.
[109] J. Toonstra and W. Kinsner. Transient Analysis and Genetic Algorithms for Classification. IEEE WESCANEX 95. 1995, 432-437.
[110] M. Srinivas, L. M. Paynaik, Genetic algorithms: A survey, IEEE computer, June 1994, 17-26.
[111] 蔡权伟，多分量信号的信号分量分离技术研究：[博士学位论文]，电子科技大学，2006．
[112] F. Jondral, Automatic classification of high frequency signals, Signal Processing, October 1985, 9(3): 177-190.
[113] Gardner. W, Spectral correlation of modulated signals: PART I-Analogue Modulation, IEEE Trans. on Comm., 1987, 35(6): 584-594.
[114] Gardner. W, Spectral correlation of modulated signals: PART I-Digital Modulation, IEEE Trans. on Comm., 1987, 35(6): 595-601.
[115] Y. T. Chan, L. G. Gadbois, Identification of the modulation type of a signal, Signal Processing, 1989, 16: 149-154,
[116] S. Taira, E. Murakami, Automatic classification analogue modulation signals by statistical parameters, IEEE, MILCOM, Nov. 1999, 1: 202-207.
[117] Xin Ou; Xiaowei Huang; Quasi-Haar wavelet and modulation identification of digital signals. ICCCAS 2004. Communications, Circuits and Systems, June 2004, 2: 733-737.
[118] Swamia, Sadler B M. Hierarchical, Digital Modulation Using Cumulants. IEEE Trans. Communications, 2000, 48(3): 416-429.
[119] ZHAN Ya-feng, CAO Zhi-gang, MA Zheng-xin. Modulation classification of M-QAM signals. JOURNAL OF CHINA INSTITUTE OF COMMUNICATIONS. 2004, 25(2): 68-74.
[120] LI Chao, KUO Yong-hong, Qam signals recognition based on fractal research of constellation. Information Technoloyg, 2005, 3: 23-25.
[121] Drnmright, T. A.; Zhi Ding, A new algorithm for QAM signal classification in AWGN channels, IEEE International Symposium on, May 2002, 1: Ⅰ-849-Ⅰ-852
[122] Dobre, O. A.; Bar-Ness, Y.; Wei Su; Higher-order cyclic cumulants for high order modulation classification, MILCOM 2003. IEEE, 1: 112-117.
[123] Hadinejad-Mahram, H.; Hero, A. O., III; Robust QAM modulation classification via moment matrices, PIMRC 2000. The 11th IEEE International Symposium on, 1: 133-137.
[124] Soo-Chang Pei, etc. High resolution Wigner Distribution using Chirp Z-Transform analysis, Signal Processing, 1990, 161-163.
[125] 崔锦泰，小波分析导论，西安交通大学出版社，1995．
[126] Li-Chen Lin, et al. c. On the convergence of wavelet-based iterative signal extrapolation algorithms, Signal Processing, 1996, 48: 51-65.
[127] Daubechies L. The Wavelet Transform Time-Frequency Localization and Sign Analysis, IEEE Trans on Information Theory, 1990, 36(5): 961-1006.
[128] L. Cohen. Time-Frequency Distribution-A Review, Proceeding of The IEE, July 1989, 77(7): 941-981.
[129] 纪跃波，秦树人，汤宝平，Winger分布干扰项抑制及其算法．重庆大学学报(自科版)，2001，24(4)：26-30．
[130] 陈端，刘树棠，基于离散Gabor变换的抑制交叉项的新方法，西安交通大学学报，1997，31(9)：77-80．
[131] Zhang Haiyong, Ma Xiaojiang, GaiQiang. Wigner-Ville Distribution Based on Intrinsic Mode Function. 2001. Radar, 2001 CIE International Conference on, Proceedings 15-18 Oct. 2001, 1015-1017.
[132] Gai Qiang, Ma Xiaojiang, Zhang Haiyong, et al. Processing Time-Varying Signals by a New Method. Radar, 2001 CIE International Conference on, Proceedings 15-18 Oct. 2001, 1011-1014.
[133] Richaro G Baraniuk, Douglas L Jones. Signal-dependent Time-frequency Representation. Signal Processing, 1993, 32: 263-284.
[134] 王衍文，余鹏，程敬之，心脏杂音的自适应时频谱分析，生物物理学报，1999，15(2)：351-359．
[135] 陈光化，马世伟，曹家麟，基于分数阶傅立叶变换的自适应时频表示，系统工程与电子技术，2001，23(4)：69-71．
[136] 陈光化，曹家麟，王健，等，应用自适应时频分布的瞬时频率估计，系统工程与电子技术，2002，24(1)：31-34．
[137] Mallat S. G., Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on signal processing, 1993, 41(12): 3397-3415.
[138] Shie Qian, Dapang Chen. Signal Representation using adaptive normalized Gaussian function. Signal Preceessing, 1994, 36: 1-11.
[139] Norden E. Huang, Zheng Shen, Steven R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysi. Proc. R. Soc. Lond. A, 1998, 454: 903-995.
[140] Lob CH, et al. Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses. BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2001, 91(5): 1339-1357.
[141] Echeverria J. C., et al. Application of empirical mode decomposition to heart rate variability analysis[J]. Medical & Biological Engineering & Computing, 2001, 39: 471-479.
[142] Ivan Magrin-Chagnolleon, Richard G Empirical mode decomposition based time-frequency attributes. Proc. of the 69th SEG Meeting, 1999, 41-60.
[143] Huang N. E, Shen Z, Long SR, et al. A new method for nonlinear and nonstationary time series analysis. 4th international conference on stochastic structural dynamics, 1999, 559-564.
[144] Huang N. E., Review of Empirical Mode Decomposition. Proc. of SPIE, 2001, 4391: 71-79.
[145] Huang N. E., A New method for nonlinear and nonstationary time series analysis: Empirical mode decomposition and Hilbert spectral analysis. Proc. of SPIE, 2000, 4056: 197-209.
[146] Timashev SA, Shalin MG. Precise Analysis of Non-stationary Vibration Processes Using the Hilbert Transform. ACSIM 2000 Proceeding, 2000, 395-404.
[147] Wang N., Empirical mode decomposition and acoustic inversion of discrete layered media. Acoustics Letters, 2001, 23(7): 145-148.
[148] Norden E. Huang, Z. S., Stever R. Long, et al., A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
[149] T.Schlurmann. Spectral Frequency Analysis of Nonlinear Water Waves Based on the Hilbert-Huang Transformation. Proceeding of OMAE'01,20th international Conference on Offshore Mechanics and Arctic Engineering, 411-418.
[150] Komm RW, H. F., Empirical mode decomposition and Hilbert analysis applied to rotation residuals of the solar convection zone. Astrophysical Journal, 2001, 559(1):428-441.
[151] Dionisio Bernal, Burcu Gunes, An Examination of Instantaneous Frequency as a Damage Detection Tool. Department of Civil and Environmental Engineering, 427 Spell Engineering Center, Northeastern University, Boston, MA 02115, U. S. A.
[152] H. T. Vincent, S. L.H., Z.Hou, Damage Detection Using Empitical Mode Decomposition Method and a Comparison with Wavelet Analysis. Proc. of the 2nd international workshop on structural health monitoring, 2000.
[153] Yang Jann N., L. Y., System identification of linear structures using Hilbert transform and Empirical Mode Decomposition. Proceedings of the International Modal Analysis Conference, 2000,213-219.
[154] Kang Huang, Dave Tateh Chang, Thomas Hou. A Bridge Monitoring Method Based on Vibration Characteristics Under a Transient Load. Proceeding of the International Symposium of Civil Engineering in the 21th Century, Beijing, China, 2000, 563-565.
[155] Gravi er B. M., N. N. J., Pelstring .T. A., An assessment of the application of the Hilbert spectrum to the fatigue analysis of marine risers. Proceedings of the International Offshore Yue Huanyin> G. H, Han Chunming, Li Xinwu, et al, A SAR Interferogram Filter Based on the Empirical Mode Decomposition Method. Geoscience and Remote Sensing Symposium, ICARSS'01. IEEE 2001 International, 2001, 5:2061-2063.
[156] 邓拥军,王伟,钱成春, EMD 方法及Hilbert 变换中边界问题的处理。科学通报,2001, 46 (3): 257-263.
[157] O. Bessen, P. Stoica, Frequency estimation and detection for sinusoidal signals with arbitrary envelope: a nonlinear least-squares approach, Proc. IEEE ICASSP-98, 1998, 4:2209-2212.
[158] B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals, Proc.IEEE, April 1992, 80(4):520-538.
[159] B. Boashash, Estimating and interpreting the instantaneous frequency of a signal. II. Algorithms and applications, Proc.IEEE, April 1992, 80(4):540-568.
[160] R.J. Mammone, R.J. Rothaker, C.I. Podilchuk, Estimation of Carrier Frequency, Modulation Type and Bit Rate of An Unknown Modulated Signal, ICC'87, 1987, 1006-1012.
[161] LiangHong, K. C. Ho, Identification of Digital Modulation Type Using The Wavelete Transform, IEEE MILCOM 1999, 1: 427-431.
[162] Shenna M. The Discrete Wavelete Transform: Wedding the A-irous and Mallat Algorithm, IEEE Trans. On SP, 1992, SP-40(10): 2464-2482.
[163] 何哲平，肖先赐，PSK信号检测的小波变换方法，中国电子学会电子对抗第五届学术年会论文集，1997年10月，成都，684-687．
[164] K. C. Ho, W. Prokopiw, et al, Modulation Identification of Digital Signal by the Wavelet Transform, IEE Proc. Radar, Sonar Navig, 2000, 147(4): 169-176.
[165] Hermann Roh Ling, Radar CFAR Thresholding in Clutter and Multiple Target Situation, IEEE Trans. on AES, 1983, AES-19(47): 608-620.
[166] Masaaki SHIBUKI, Tsutomu SUGIYAMA, Ken Iwasaki, Transmitter Identification—The Development of a High speed Data Acquisition System with Receiving Functions, Journal of the Communications Research Laboratory, 2002, 49(1): 129-135.
[167] D. Fox etal, Bayesian Filtering for location estimation, Pervasive Computing. 2003, 24-33.
[168] S. J. Russell and P. Norvig. Artificial Intelligence: Modem Approach. Prentice Hall, 2002.
[169] Camastra F, Vinciareli A. Intrinsic dimension estimation of data: An approach based on Grassber-Procaccia's algorithm. Neural Processing Letters, 2001, 14(1): 27-34.
[170] 蒋东翔，黄文虎，徐世昌，分形几何及其在故障诊断中的应用，哈尔滨工业大学学报，1996，28(2)：27～31．
[171] 张文明，李莉，申焱华，滚动轴承故障诊断中的分形．北京科技大学学报，1996，18(3)：215～219．
[172] Zhang G X, Jin w D，Hu L z. Radar Emitter Signal Recognition Based on Complexity Features. Journal of Sounthwest Jiaotong University, 2004, 12(2): 116-122.
[173] Montesino Otero M E Rolo Naranjo A, Aoy Carralero A. The attractor dimension determination applied to monitoring and surveillance in nuclear power plants. Progress in Nucear Energy, 2003, 43(1): 389-395.
[174] Logan D., Mathew J., Using the correlation dimension for vibration fault diagnosis of roll ingelement bearing-basic concepts. Mechanical Systems and Signal Processing, 1996, 10(3): 241-250.
[175] 吕铁军，魏平，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．
[176] Takers F. Detecting strange attractor in turbulence, in Dynamical systems and turbulence, Warwich, 1980 Lecture Notes in Mathematics, Vol. 898, Rand and Young eds, 1981: 366-381.
[177] H Whitney. Theself-intersections of a smoothn-manifold in 2n-space. AnnMath, 1994, 45: 220-246.
[178] D. R. J. Chilling worth. Differential Topology with a View to Applications. Loton: Pitman, 1976.
[179] Logan D., Mathew J., Using the correlation dimension for vibration fault diagnosis of roll ingelement bearing-basic concepts[J]. Mechanical Systems and Signal Processing, 1996, 10(3): 241～250.
[180] 姜建东，屈梁生，相关维数在大机组故障诊断中的应用，西安交通大学学报，1998，32(4)：27～31．
[181] Grassberger E, Procaccia I. Characterization of strange attractors. Phys. Rev. Lett. A, 1983, 50(5): 346～349.
[182] 汪慰军，陈进，吴昭同，等，关联维数的计算及其在大机组故障诊断中的应用，上海交通大学学报，2000，34(9)：1265-1268．
[183] 谢和平，薛秀谦，分形应用中的数学基础与方法，北京：科学出版社，1997．
[184] Takers F. Detecting strange attractor in turbulence, in Dynamical systems and turbulence, Warwich, 1980 Lecture Notes in Mathematics, Vol. 898, Rand and Young eds, 1981, 366-381.
[185] H Whitney. The self-intersections of a smooth n-manifold in 2n-space. Ann Math, 1994, 45: 220-246.
[186] 林梓，李魁俊，王丽，等，混沌吸引子的重构，长春邮电学院学报，1999，17(3)：7-12．
[187] Beylkin G., Cofiman R., Rokhlin V., Fast Wavelet Transform and Numberical Algorithms Ⅰ, Comm. On Pure and Appl. Math 1991, 44(2): 141-183.
[188] 王祖林，周萌清，多重分形谱及其计算，北京航空航天大学学报，2000，26(3)：256-258．
[189] L. Sun and W. Kinsner, Characterization and Feature Extraction of Transient Signals Using Multifractal Measures, IEEE Canadian Conference on Electrical and Computer Engineering, May 1999, 2: 781-785.
[190] Yakov Lantsman and John A., Actuarial Application of Multifractal Modeling, 2001 Winter Forum.
[191] 杜干，张守宏，基于多重分形的雷达目标的模糊检测，自动化学报，2001，27(2)：174-179．
[192] J. Chen and W. Kinsner, Multifractal Analysis of Transients in Power Systems, Electrical and Computer Engineering, 2000 Canadian Conference on, 2000, 1: 307-311.
[193] A. K. Nandi, E. E. Azzouz, Modulation recognition using artificial neural networks, Signal processing, 1997, 56: 165-175.
[194] Q. Jiang, S. S. Goh, Z. Lin, Local discriminant time-frequency atoms for signal classfication, Signal processing, 1999, 72:47-52.
[195] L. V. Dominguez, J. M. P. Borrallo, J. P. Garcia, A general approach to the automatic classification of radiocommunication signals, Signal processing, 1991, 22:239-250.
[196] Yawpo yang, Ching-hwa liu, Ta-wei soong, A log-likelihood function-based algorithm for QAM signal classification, Signal processing, 1998, 70:61-71.
[197] D.E.Eckhardt, JR. and L.D.Lee, A theoretical basis for the analysis of multiversion software subject to coincident errors, IEEE Trans, on Software Engineering, Dec 1985, SE-11(12):1511-1517.
[198] D. Fox et al. Bayesian Filtering for location estimation. Pervasive Computing. 2003, 24-33.
[199] J. Toonstra and W. Kinsner. Transient Analysis and Genetic Algorithms for Classification. IEEE WESCANEX 95, 1995, 432-437.
[200] 张葛祥,李娜,金炜东,等,一种新量子遗传算法及其应用,电子学报, 2004,32(3): 476-479.
[201] Li B, Zhuang Z Q, Genetic Algorithm Based-on the Quantum Probability Representation, Lecture Notes in Computer Science, 2002, 2412:500-506.
[202] X.M. Huo, D. Donoho, A Simple and Robust Modulation Classification Method Via counting, ICASSP'98, 1998, 3289-3292.
[203] V.J. Stolpman, S. Paranjpe, G. C. Orsak, A Blind Information Theoretic Approach to Automatic Signal Classification, Milcom'99, 1999, 447-451.
[204] Shi D, Shu W H,Liu H T. Feature seection for handwritten Chinese character recognition based on genetic agorithms. Proceedings of IEEE International Conference on System, Man, and Cybernetics. 1998, 4201-4206.
[205] Mitra P, Murthy C A, Pal S k. Unsuoervised feature selection using feature similarity. IEEE Transacions on Pattern Anaysis and Machine Intelligence, 2002, 24(3):301-312.
[206] Molina L. C, Belanche L., Nebot A., Feature selection algorithms: a survey and ecperimenta evauation. Proceedings of Internationa Conference on Data Mining, 2002, 306-313.
[207] Bressan M., Vitria J., On the selection and classification of independent features, IEEE Trans. on Pattern Anaysis and Machine Intelligence, 2003, 25(10):1312-1317.
[208] Guo G. D., Dyer C. R., Simultaneous selection and cassifier training via linear programming: a case study for face expression recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 346-352.
[209] 吕铁军，王河，肖先赐，新特征选择方法下的信号调制识别，电子与信息学报，2002，24(5)：661-666．
[210] Zhang G. X., Hu L. Z., Jin W. D., Resembance coefficient and a quantum genetic agorithm for feature selection. Lecture Notes in Artificial Intelligence, 2004, 3245: 155-168.