软计算理论及其在信号处理中的应用
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摘要
随着通信、计算机、电子学科的不断发展,其工程应用日趋广泛和深入,所采用的数学方法也随之不断地丰富,在大量的工程应用中由于信息处理的需求,软计算理论和方法得到大量的应用,其自身理论也在不断的发展,软计算与其它方法的结合应用也在不断出现,使之在信息处理应用中显现出极大的重要性,本文主要对软计算方法(包括:小波理论、人工神经网络、分形理论、混沌理论、模糊数学等)在信息处理中的应用进行了深入的研究,探讨了软计算理论及其它相关数学理论的融合进行信息处理的方法,并进行计算机仿真实验,仿真实验结果表明,该文提出的几种方法是有效的。
本文详细而具体地讨论和研究了小波理论、人工神经网络、分形理论、混沌理论、模糊数学在超宽带通信和微弱信号检测的应用研究。综合运用这些技术以构建全新的超宽带多用户检测、微弱谐波信号检测方法和多传感器多目标跟踪系统。
首先,在通信信号处理领域,对超宽带通信中的多用户检测问题进行了深入分析,提出了两种多用户检测算法。在超宽带通信领域,提出了基于隐马尔可夫模型和Kalman滤波的多用户检测算法。
算法1:该算法将跳时二进制相移键控超宽带通信系统建模为隐马尔可夫系统模型。考虑到发射序列状态之间可能存在时序的非继承性,而搜索与前向序列对应的具有最大转移概率的后续序列,提出了最大后验概率(MAP)多用户检测算法,模拟结果表明了该算法的有效性。
算法2:该算法将跳时二进制相移键控超宽带通信系统建模为动态系统模型,然后利用Kalman滤波算法,设计了一种新的超宽带盲自适应多用户检测算法。数值模拟结果表明,该方法比传统的检测器有更低的误码率,说明该算法是一种有效的检测算法。
其次,对微弱谐波信号难于检测的问题,针对强混沌背景和强分形噪声背景干扰下的微弱谐波信号检测问题,提出了相应的解决方案。
对强混沌背景噪声干扰下的微弱谐波信号检测问题。利用RBF神经网络预测混沌时间序列的能力建立预测模型,用模糊聚类算法来获得RBF神经网络的径向中心,将观测信号减去预测出的混沌信号,得到误差信号,而误差信号中可能含有有用微弱信号及普通的白噪声。然后把误差信号送入Duffing混沌振子,充分利用Duffing混沌振子对噪声的免疫性,来检测是否含有微弱信号。该方法充分利用了模糊聚类,神经网络,混沌时间序列的短期可预测性,从而大大提高检测微弱信号的性能。仿真实验结果表明:该方法能够在信噪比-120dB时检测出微弱谐波信号。
而对强分形噪声干扰下的微弱谐波信号检测问题。本文提出了小波域多尺度模糊自适应Kalman滤波和Duffing混沌振子相结合的方法来解决强分形背景干扰下的谐波信号检测问题。先对淹没在强分形随机背景干扰中的观测信号进行多尺度小波变换,根据分形随机信号小波系数的平稳性,建立状态方程和观测方程,利用Kalman滤波,对每一尺度进行进行滤波最后估计出分形随机信号,并对噪声方差未知时,利用模糊推理系统来动态逼近,然后将估计信号与观测信号作差得到误差信号,把误差信号送入Duffing混沌振子,充分利用Duffing混沌振子对噪声的免疫性,来检测是否含有微弱信号。仿真实验结果表明:该方法能在低信噪比和低信干比下有效地检测出淹没在强分形噪声中的微弱谐波信号。
同时,文中也给出了Duffing混沌振子的免疫性一种新的统计解释。
最后,对多传感器多目标跟踪问题提出了基于模糊聚类和JPDA的多传感器多目标跟踪算法。传统的联合概率数据关联算法(JPDA)是适合于密集杂波条件下的一种良好的多目标跟踪算法,但它是针对单传感器对多目标跟踪的情况下使用的,不能直接用于多传感器多目标的跟踪,并且随着目标数和回波数的增加,其运算量出现了组合爆炸现象,为此提出了一种新简化的JPDA算法,该算法适用于多传感器多目标跟踪的情况。该算法首先利用极大似然估计对多传感器的测量集合进行同源划分,然后对来自同一目标的测量进行融合,然后采用改进的模糊C-均值(FCM)聚类算法计算关联概率,采用JPDA算法的类似结构对目标航迹进行更新和修正。仿真实验结果说明了该方法能有效地进行多传感器多目标的跟踪,并且算法简单、跟踪精度高、计算量小、易于工程实现。
With the development of communication, computer and electronics, the en-gineering applications have become more widely and more deeply, the number ofmathematical methods increases rapidly in abundant engineering applications. Be-cause of the need of information processing, soft computing methods and othermathematical methods are used widely. The theory of soft computing developedrapidly, and some other relative theories appear constantly, which increases its im-portance in the application of information processing. This thesis investigates theuse of soft computing methods (including: wavelet theory, artificial neural network,fractional theory, chaos theory and fuzzy mathematics, etc) in information process-ing, reveals the problems in engineering applications of soft computing methods andcombines different mathematics methods to realize information processing.
This thesis investigates and discusses the use of soft computing methods inultra-wide band(UWB) communications, weak signal detection and multi-sensormulti-target tracking. These techniques are used to construct novel ultra-wide bandcommunication multi-user detectors, weak signal detection systems and multi-senormulti-target tracking system.
Firstly, in digital communication signal processing fields, two multi-user de-tection methods are proposed. For impulse radio UWB systems based on the proba-bility and statistics, the first novel multi-user detection is presented. HMM systemsof time-hopping BPSK in UWB are developed separately in the paper. Consideringseeking for the following sequence of maximal transition probability correspond-ing to previous sequence, a maximum a posteriori (MAP) multi-user detector hasbeen constructed. The second novel blind multi-user adaptive detection algorithm ispresented. Dynamical systems of time-hopping BPSK in UWB are developed sepa-rately in the paper. multi-user detector has been constructed using the recently pro-posed canonical representation of multi-user receivers and Kalman filtering. The-oretical analysis and numerical results show that the new scheme has better BERperformance comparing with the conventional detector.
Secondly, in digital signal processing, for strong chaotic noise and fractionalnoise, two weak signal detection schemes are investigated respectively. A novel approach of weak sinusoid signal detection from a chaotic noise background is pro-posed. The RBF neural networks are used to predict the chaotic background, and theradial center is gained by fuzzy c-means algorithm. The noisy error signal extractedfrom the detected signal, then the error signal is added into the Duffing chaotic os-cillator using the sensitivities to the initial conditions and immunity to noise of thesystem. Based on the motion transition of a chaotic system, new schemes to de-tect weak sinusoidal signal berried in a chaotic background are presented forward.Simulation measured data demonstrate the effectiveness of the proposed algorithm.Weak signal detection of strong fractional Brownian motion (fBm)using Duffingoscillator and fuzzy adaptive Kalman filter is proposed in this paper. A dynamicsystems based on the orthonormal wavelet decomposition coefficients of the fBm isformulated, fuzzy adaptive Kalman filter is used to estimate the fBm when the vari-ance of measurement noise is unknown. The noisy error signal extracted from thedetected signal, then the error signal is added into Duffing chaotic oscillator usingthe sensitivities to the initial conditions and immunity to noise of the system. Basedon the motion transition of a chaotic system and the estimation of fuzzy adaptiveKalman filtering, new schemes to detect weak sinusoidal signal berried in a strongfractional background noise are presented forward. Simulation results demonstratethe effectiveness of the proposed algorithm.
A new statistical understanding of immunity to noise of Duffing systems is alsopresented.
Finally, the Joint Probabilistic Data Association (JPDA) solves single sensormulti-target tracking in clutter, but it can not be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the numbers oftargets and the number of returns. The novel algorithm was presented by combiningMaximum Likelihood Estimation (MLE) with Fuzzy C-Means (FCM) clustering.The MLE is used to classify the same source observations at one time into the sameset, then the FCM approach is used to calculate the data association probability, andthe similarity structure of JPDA algorithm is used to realize the MMT. The computersimulations indicate that the scheme achieve MMT perfectly with low computationalcomplexity, higher precision and easy realization.
本文详细而具体地讨论和研究了小波理论、人工神经网络、分形理论、混沌理论、模糊数学在超宽带通信和微弱信号检测的应用研究。综合运用这些技术以构建全新的超宽带多用户检测、微弱谐波信号检测方法和多传感器多目标跟踪系统。
首先,在通信信号处理领域,对超宽带通信中的多用户检测问题进行了深入分析,提出了两种多用户检测算法。在超宽带通信领域,提出了基于隐马尔可夫模型和Kalman滤波的多用户检测算法。
算法1:该算法将跳时二进制相移键控超宽带通信系统建模为隐马尔可夫系统模型。考虑到发射序列状态之间可能存在时序的非继承性,而搜索与前向序列对应的具有最大转移概率的后续序列,提出了最大后验概率(MAP)多用户检测算法,模拟结果表明了该算法的有效性。
算法2:该算法将跳时二进制相移键控超宽带通信系统建模为动态系统模型,然后利用Kalman滤波算法,设计了一种新的超宽带盲自适应多用户检测算法。数值模拟结果表明,该方法比传统的检测器有更低的误码率,说明该算法是一种有效的检测算法。
其次,对微弱谐波信号难于检测的问题,针对强混沌背景和强分形噪声背景干扰下的微弱谐波信号检测问题,提出了相应的解决方案。
对强混沌背景噪声干扰下的微弱谐波信号检测问题。利用RBF神经网络预测混沌时间序列的能力建立预测模型,用模糊聚类算法来获得RBF神经网络的径向中心,将观测信号减去预测出的混沌信号,得到误差信号,而误差信号中可能含有有用微弱信号及普通的白噪声。然后把误差信号送入Duffing混沌振子,充分利用Duffing混沌振子对噪声的免疫性,来检测是否含有微弱信号。该方法充分利用了模糊聚类,神经网络,混沌时间序列的短期可预测性,从而大大提高检测微弱信号的性能。仿真实验结果表明:该方法能够在信噪比-120dB时检测出微弱谐波信号。
而对强分形噪声干扰下的微弱谐波信号检测问题。本文提出了小波域多尺度模糊自适应Kalman滤波和Duffing混沌振子相结合的方法来解决强分形背景干扰下的谐波信号检测问题。先对淹没在强分形随机背景干扰中的观测信号进行多尺度小波变换,根据分形随机信号小波系数的平稳性,建立状态方程和观测方程,利用Kalman滤波,对每一尺度进行进行滤波最后估计出分形随机信号,并对噪声方差未知时,利用模糊推理系统来动态逼近,然后将估计信号与观测信号作差得到误差信号,把误差信号送入Duffing混沌振子,充分利用Duffing混沌振子对噪声的免疫性,来检测是否含有微弱信号。仿真实验结果表明:该方法能在低信噪比和低信干比下有效地检测出淹没在强分形噪声中的微弱谐波信号。
同时,文中也给出了Duffing混沌振子的免疫性一种新的统计解释。
最后,对多传感器多目标跟踪问题提出了基于模糊聚类和JPDA的多传感器多目标跟踪算法。传统的联合概率数据关联算法(JPDA)是适合于密集杂波条件下的一种良好的多目标跟踪算法,但它是针对单传感器对多目标跟踪的情况下使用的,不能直接用于多传感器多目标的跟踪,并且随着目标数和回波数的增加,其运算量出现了组合爆炸现象,为此提出了一种新简化的JPDA算法,该算法适用于多传感器多目标跟踪的情况。该算法首先利用极大似然估计对多传感器的测量集合进行同源划分,然后对来自同一目标的测量进行融合,然后采用改进的模糊C-均值(FCM)聚类算法计算关联概率,采用JPDA算法的类似结构对目标航迹进行更新和修正。仿真实验结果说明了该方法能有效地进行多传感器多目标的跟踪,并且算法简单、跟踪精度高、计算量小、易于工程实现。
With the development of communication, computer and electronics, the en-gineering applications have become more widely and more deeply, the number ofmathematical methods increases rapidly in abundant engineering applications. Be-cause of the need of information processing, soft computing methods and othermathematical methods are used widely. The theory of soft computing developedrapidly, and some other relative theories appear constantly, which increases its im-portance in the application of information processing. This thesis investigates theuse of soft computing methods (including: wavelet theory, artificial neural network,fractional theory, chaos theory and fuzzy mathematics, etc) in information process-ing, reveals the problems in engineering applications of soft computing methods andcombines different mathematics methods to realize information processing.
This thesis investigates and discusses the use of soft computing methods inultra-wide band(UWB) communications, weak signal detection and multi-sensormulti-target tracking. These techniques are used to construct novel ultra-wide bandcommunication multi-user detectors, weak signal detection systems and multi-senormulti-target tracking system.
Firstly, in digital communication signal processing fields, two multi-user de-tection methods are proposed. For impulse radio UWB systems based on the proba-bility and statistics, the first novel multi-user detection is presented. HMM systemsof time-hopping BPSK in UWB are developed separately in the paper. Consideringseeking for the following sequence of maximal transition probability correspond-ing to previous sequence, a maximum a posteriori (MAP) multi-user detector hasbeen constructed. The second novel blind multi-user adaptive detection algorithm ispresented. Dynamical systems of time-hopping BPSK in UWB are developed sepa-rately in the paper. multi-user detector has been constructed using the recently pro-posed canonical representation of multi-user receivers and Kalman filtering. The-oretical analysis and numerical results show that the new scheme has better BERperformance comparing with the conventional detector.
Secondly, in digital signal processing, for strong chaotic noise and fractionalnoise, two weak signal detection schemes are investigated respectively. A novel approach of weak sinusoid signal detection from a chaotic noise background is pro-posed. The RBF neural networks are used to predict the chaotic background, and theradial center is gained by fuzzy c-means algorithm. The noisy error signal extractedfrom the detected signal, then the error signal is added into the Duffing chaotic os-cillator using the sensitivities to the initial conditions and immunity to noise of thesystem. Based on the motion transition of a chaotic system, new schemes to de-tect weak sinusoidal signal berried in a chaotic background are presented forward.Simulation measured data demonstrate the effectiveness of the proposed algorithm.Weak signal detection of strong fractional Brownian motion (fBm)using Duffingoscillator and fuzzy adaptive Kalman filter is proposed in this paper. A dynamicsystems based on the orthonormal wavelet decomposition coefficients of the fBm isformulated, fuzzy adaptive Kalman filter is used to estimate the fBm when the vari-ance of measurement noise is unknown. The noisy error signal extracted from thedetected signal, then the error signal is added into Duffing chaotic oscillator usingthe sensitivities to the initial conditions and immunity to noise of the system. Basedon the motion transition of a chaotic system and the estimation of fuzzy adaptiveKalman filtering, new schemes to detect weak sinusoidal signal berried in a strongfractional background noise are presented forward. Simulation results demonstratethe effectiveness of the proposed algorithm.
A new statistical understanding of immunity to noise of Duffing systems is alsopresented.
Finally, the Joint Probabilistic Data Association (JPDA) solves single sensormulti-target tracking in clutter, but it can not be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the numbers oftargets and the number of returns. The novel algorithm was presented by combiningMaximum Likelihood Estimation (MLE) with Fuzzy C-Means (FCM) clustering.The MLE is used to classify the same source observations at one time into the sameset, then the FCM approach is used to calculate the data association probability, andthe similarity structure of JPDA algorithm is used to realize the MMT. The computersimulations indicate that the scheme achieve MMT perfectly with low computationalcomplexity, higher precision and easy realization.
引文
[1] B. B. Mandelbrot and J. W. Van Ness. Fractional Brownian Motions, FractionalNoises and Applications[J]. SIAM Rev., Vol. 10, No. 4, October, 1968, pp.422-437.
[2] G. A. Hirchoren, C. E. D’Alttellis. Estimation of Fractional Brownian Motionwith Multiresolution Kalman Filter Banks[J]. IEEE Trans. on Signal Process-ing, Vol. 47, No. 5, May 1999, pp. 1431-1434.
[3] Gustavo A. Hirchoren, Carlos E. D’Attellis. Estimation of Fractal Signals Us-ing Wavelets and Filter Banks[J]. IEEE Trans. on Signal Processing, Vol. 46,No. 6, June 1998, pp. 1624-1630.
[4] Lei Zhang, Paul Bao, Xiaolin Wu. Wavelet Estimation of Fractional BrownianMotion Embedded in a Noisy Environment[J]. IEEE Trans. on InformationTheory, Vol. 50, No. 9, September, 2004, pp. 2194-2200.
[5] G. W. Wornell, A. V. Oppenheim. Estimation of Fractal Systems from NoisyMeasurements Using Wavelets[J]. IEEE. Trans. on Signal Processing, 1992,40(3):611-622.
[6] 曹坤勇,于盛林。基于正交小波变换的噪声下1/f类分形信号的恢复[J]。南京航空航天大学学报,2004,36(2):245-248。CAO Kun-yong, YU Sheng-lin. Restoration of Noisy 1/f-Type Fractal SignalBased on Orthonormal Wavelet Transformation[J]. Journal of Nanjing Univer-sity of Aeronautics & Astronautics, Vol. 36, No. 2, April, 2004, pp. 245-248.
[7] Shoumin Liu, Zhi Tian. A Kalman-PDA Approach to Soft-Decision Equal-ization for Frequency-Selective MIMO Channels[J]. IEEE Trans. on SignalProcessing, Vol. 53, No. 10, October, 2005, pp. 3819-3830.
[8] Ning Ma, Martin Bouchard, Rafik A. Goubran. Speech Enhancement Usinga Masking Threshold Constrained Kalman Filter and Its Heuristic Implemen-tations[J]. IEEE Trans. on Audio, Speech, and Language Processing, Vol. 14,No. 1, January, 2006, pp. 19-32.
[9] Guangyu Wang, Sailing He. A quantitative study on detection and estimationof weak signals by using chaotic Duffing oscillators [J]. IEEE Trans. on Cir-cuits and Systems-I: Fundamental Theory and applications, 2003, 50(7): 945-953.
[10] 李月,杨宝俊,林洪波,刘晓华。基于特定混沌系统微弱谐波信号频率检测的理论分析与仿真[J]。物理学报,2005,54(5):1994-1999。Li Yue, Yang Bao-Jun, Lin Hong-Bo, et al. Simulation and theoretical analysison detection of the frequency of weak harmonic signals based on a specialchaotic system [J]. ACTA PHYSICA SINICA, 2005,54(5): 1994-1999.
[11] 尚秋峰,尹成群,李士林等。基于Duffing振子的微弱正弦信号检测方法研究[J]。中国电机工程学报,2005,25(2):66-70。SHANGQiu-feng, YIN Cheng-qun, LI Shi-lin, et al. Study on detection ofweak sinusoidal signal by using Duffing oscillator [J]. Proceedings of theCSEE, 2005,25(2):66-70.
[12] P. Flandrin. Wavelet Analysis and synthesis of fractional Brownian Motion[J].IEEE Trans. on Information Theory, 1992, 38(2):910-917.
[13] 王冠宇,陈大军,林建亚等。Duffing振子微弱信号检测方法的统计特性研究[J]。电子学报,1998,26(10):38-44。Guangyu Wang, Dajun Chen, Jingya Lin et al. The statistical characteristics ofweak signal detection based on Duffing oscillator [J]. 1998,26(10): 38-44.
[14] 李月,杨宝俊,石要武。色噪声背景下微弱正弦信号的混沌检测[J]。物理学报,2003,52(3):526-530。Li Yue, Yang Bao-Jun, Shi Yao-Wu. Chaos-based weak sinusoidal signaldetection approach under colored noise background [J]. ACTA PHYSICASINICA, 2003,52(3): 526-530.
[15] G. Wornell. Et al. ,Wavelet based representation for a class of self-similarsignals with application to fractal modulation, IEEE Trans. ,IT-38(2),785800,1992
[16] Neil F Johnson, An Introduction to Watermark Recovery from Images[A], bySANS Intrusion Detection and Response(ID’99)[C], San Diego, CA.:ID,1999
[17] 李世雄,小波变换及其应用,高等教育出版社,1997
[18] 罗建书,小波分析及其应用[M],电子工业出版社,1992
[19] 彭玉华,小波变换与工程应用[M],科学出版社,1999
[20] 杨福生,小波变换的工程分析与应用[M],科学出版社,2000
[21] 董连科,分形理论及其应用,辽宁出版社,1991
[22] 李水根,吴纪桃,分形与小波,科学出版社,2002
[23] 陈守吉,张立明,分形与图像压缩,上海科技教育出版社,1998
[24] 毛用才,胡奇英,随机过程[M],西安电子科技大学出版社,1999
[25] 汪荣鑫,随机过程[M],西安交通大学出版社,1988
[26] 王衍波,薛通,应用密码学,机械工业出版社,2003
[27] Simon Haykin, Sadasivan Puthusserypady. Chaotic Dynamics of Sea Clutter[M]. John Wiley & Sons, Inc, 1999.
[28] Simon Haykon, Jose Principe. Using neural networks to dynamically modelchaotic events such as sea clutter [J]. IEEE Singal Processing Magazine, May,1998, 15(66): 81.
[29] He Jiang-hua, Yang Zhong-kai, Wang Shu. Transient signal detection based onchaos and neural network [J]. ACTA ELECTRONICA SINICA, 1998, 26(10):33-37.何 建 华。 基 于 混 沌 和 神 经 网 络 的 弱 信 号 探 测[J]。 电 子 学报,1998,26(10):33-37。
[30] Guangyu Wang, Dajun Chen, Jingya Lin et al. The statistical characteristics ofweak signal detection based on Duffing oscillator [J]. 1998,26(10): 38-44.王冠宇,陈大军,林建亚等。Duffing振子微弱信号检测方法的统计特性研究[J]。电子学报,1998,26(10):38-44。
[31] Guangyu Wang, Dajun Chen, Jingya Lin et al. The application of chaotic os-cillators to weak signal detection [J]. IEEE Trans. on Industrial Electronics,1999, 46(2): 440-444.
[32] Guangyu Wang, Sailing He. A quantitative study on detection and estimationof weak signals by using chaotic Duffing oscillators [J]. IEEE Trans. on Cir-cuits and Systems-I: Fundamental Theory and applications, 2003, 50(7): 945-953.
[33] Li Yue, Yang Bao-Jun, Shi Yao-Wu. Chaos-based weak sinusoidal signaldetection approach under colored noise background [J]. ACTA PHYSICASINICA, 2003,52(3): 526-530.李月,杨宝俊,石要武。色噪声背景下微弱正弦信号的混沌检测[J]。物理学报,2003,52(3):526-530。
[34] Li Yue, Yang Bao-Jun, Lin Hong-Bo, et al. Simulation and theoretical analysison detection of the frequency of weak harmonic signals based on a specialchaotic system [J]. ACTA PHYSICA SINICA, 2005,54(5): 1994-1999.李月,杨宝俊,林洪波,刘晓华。基于特定混沌系统微弱谐波信号频率检测的理论分析与仿真[J]。物理学报,2005,54(5):1994-1999。
[35] Leung H, Wang S. C. Prediction of noisy chaotic time series using an optimalradial basis function neural network [J]. IEEE Trans. Neural Networks, 2001,12(25): 1163-1172.
[36] Schilling R. J., Carroll J. J. and AL-Ajlouni A. F. Approximation of nonlinearsystems with radial basis function neural networks [J]. IEEE Trans. NeuralNetworks, 2001,12(1): 1-14.
[37] SHANGQiu-feng, YIN Cheng-qun, LI Shi-lin, et al. Study on detection ofweak sinusoidal signal by using Duffing oscillator [J]. Proceedings of theCSEE, 2005,25(2):66-70.尚秋峰,尹成群,李士林等。基于Duffing振子的微弱正弦信号检测方法研究[J]。中国电机工程学报,2005,25(2):66-70。
[38] LU Peng, LI Yue. A modified chaos-based weak sinusoidal signal amplitudedetection approach [J]. ACTA ELECTRONICA SINICA , 2005, 33(3): 527-529.路鹏,李月。微弱正弦信号幅值混沌检测的一种改进方案[J]。电子学报,2005,33(3):527-529。
[39] Jiang Zheng-xin, Shi Guo-liang. Matrix theory and its application [M], Beijingaeronautic and astronautic university press, Beijing, 1988.将正新,施国梁,矩阵理论及其应用[M],北京航空学院出版社,北京,1988。
[40] Cheng Yun-peng. Matrix theory (the second edition) [M], Northwest industryuniversity press, Shanxi, 2002.程云鹏,矩阵论(第二版)[M],西北工业大学出版社,陕西,2002。
[41] M. Z. Win and R. A. Scholtz. Ultra-Wide Bandwidth Time-Hopping Spread-Spectrum Impulse Radio for Wireless Multiple-Access Communications.IEEE Trans. Comm.. Vol.48. No.4, April 2002, pp.679-691.
[42] L. Fullerton, UWB waveforms and coding for communications and radar, Proc.IEEE Tele-systems Conf. , Mar. 26-27, 1991, pp. 139-141.
[43] M. Z. Win and R. A. Scholtz. On the robustness of ultra-wideband width sig-nals in dense multi-path environments, IEEE Communication letters, vol.2, pp.51-53, Feb. 1998.
[44] M. Z. Win and R. A. Scholtz. On the energy capture of ultra-wideband widthsignals in dense multi-path environments, IEEE Communication letters, vol.2,pp. 245-247, Sept. 1998.
[45] F. Ramirez-Mirels, On the performance of ultra-wideband signals in gaussianand dense multipath, IEEE Trans. Vehicular Tech., vol. 2, pp. 244-249, Jan.2001.
[46] M. Hamalainen, V. Hovinen, et al, On the UWB System Coexistence withGSM900, UMTS/WCDMA and GPS, IEEE Journal Select Areas Commun.,Vol.20, Dec. 2002, pp.1712-1721.
[47] R. A. Scholtz. Multiple Access with Time-Hopping Impulse Modulation, Proc.MILCOM’97, Oct. 1993, pp.447-450.
[48] M. Z. Win and R. A. Scholtz, Impulse Radio: How it Work, IEEE Commun.Letters, Vol.2, No.2, Feb. 1998, pp.36-38.
[49] M. Ghosh and C. L. Weber, Maximum likelihood blind equalization, in Proc.SPIE Adaptive Signal Process., 1991, vol. 1565, pp. 188-195.
[50] J. A. R. Fonollosa and J. Vidal, Application of hidden Markov models to blindchannel characterization and data detection, in Proc. IEEE Int. Conf. Acoust.,Speech, Signal process., April. 1994, pp. 185-188.
[51] M. Erkurt and J. G. Proakis, Joint data detection and channel estimation forrapidly fading channels, in Proc. IEEE Global Telecommn. Conf. 1992, pp.910-914.
[52] Carles A H, Fonolloa J A R, Zvonar Z. Probabilistic algorithms for blind adap-tive multiuser detection [J],IEEE Trans on Signal Processing, 1988,46(11), pp:2593-2966.
[53] Rabiner L E, Tutorial of Hidden Markov Models and Selected Applications onSpeech recognition [J], Proceedings of the IEEE, 1989,77 (2), pp: 257-286.
[54] 秦前清,杨宗凯,实用小波分析,西安电子科技大学出版社,1995
[55] P. P. A. Storms, F. C. R. Spieksma. An LP-based Algorithm for the Data Asso-ciation Problem in Multitarget Tracking[J]. 2003, 30: 1067-1085.
[56] Young C. Yoon, Ryuji Kohno, ”Optimum Multi-User Detection in Ultra-Wideband (UWB) Multiple-Access Communication Systems ”, Communi-caitons, 2002. ICC 2002. IEEE International Conference on, Volume:2,28April-2 May 2002, pp:812-816.
[57] Qinghua Li, and Leslie A. Rusch, Multi-user Detection for DS-CDMA UWBin the Home Environment IEEE Journal on Selected Areas in Communications,Vol.20, No.9, Dec. 2002.
[58] Ali Muqaibl, Brian Woerner and Sedki Riad, Application of Multiuser De-tection Techniques to Impulse Radio Time Hopping Multiple Access System,Ultra Wideband Systems and Technologies, 2002,Digest of Papers. 2002 IEEEConference on 2002.
[59] Arslan,H. multiaccess interference cancellation receiver for time-hopping ultrawideband communication, 2004 IEEE International Conference on ,Vol.6,20-24,June 2004,pp:3394-3398.
[60] Ping Liu, Zhengyuan Xu, Jin Tang, Minimum variance multiuser detection forimpulse radio UWB systems, Ultra wideband systems and technologies 2003IEEE international conference on , Nov.16-19,2003,pp:111-115.
[61] Le Martret, C. J, Giannnakis G. B. All digital impulse radio for MUI/ISI re-silient multiuser communications over frequency-selective multipath channels,MILCOM 2000,21st century Military Communications Conference Proceed-ings, vol. 2.
[62] Christophe J. Le Martret , C. J, Giannakis, G. B. all digital impulse radio withmultiuser detection for wireless cellular systems communications, IEEE Trans.on Vol.50 ,Issue.9, Sept. 2002.
[63] Le Martret, C. J, Giannakis, G. B. all digital PPM impulse radio for multiple-access through frequency-selective multipath sensor array and multichannelsignal processing workshop. 2000.Proceedings of the 2000 IEEE.
[64] James B C. Convergence theory for fuzzy C-means: counter examples andrepairs [J]. IEEE Transactions on Systems, Man and Cybernetics, 1987; 17(5):873-877.
[65] 严守峰,吕凤海,柳重堪. 基于最小二乘估计的电视经纬仪交会测量算法[J]. 北京航空航天大学学报,1998,24(5):588-591.YAN Shou-feng, LU Feng-hai, LIU Zhong-kan. Algorithm of movie transitintersection survey via the least square estimation [J]. Journal of Beijing Uni-versity of Aeronautics and Astronautics,1998,24(5):588-591.
[66] 杨 祖 泽. 双 点 后 方 交 会 的 数 学 模 型 及 解 算 程 序[J]. 四 川 测绘,1995,18(4):179-181.Yang Zu-ze. Mathematic Model and Computing Programmer forBackwards Double Ways Intersection [J]. Surveying and Mapping ofSichuan,1995,18(4):179-181.
[67] 柴饶军,纪大山,马彩文. 电视经纬仪复杂多目标交会测量点匹配算法[J].光电工程,2004,31(9):29-32.CHAI Rao-jun,JI Da-shan,MA Cai-wen. Points matching algorithm for so-phisticated multi-target intersection of TV theodolite [J]. Opto-Electronic En-gineering,2004,31(9):29-32.
[68] 廖传锦,黄席樾,柴毅。基于多传感器信息融合的目标跟踪与防撞决策[J]。控制理论与应用,2005; 22(1):127-133。LIAO Chuan-jin, HUANG Xi-yue, CHAI Yi. Target tracking and decision-making for collision avoidance based on multisensor fusion [J]. Control Theory& Applications, 2005; 22(1): 127-133.
[69] Blackman, S. S. Multiple target tracking with radar applications [M]. Boston:Artech House, 1986.
[70] Bar-Shalom. Y. Fortmann. T. E. Tracking and data association [M]. San Diego,CA: Academic Press, 1988.
[71] Bar-Shalom. Y. Multi-target multi-sensor tracking. Principles and Techniques[M]. New York: YBS Publishing, 1995.
[72] 李建勋,敬忠良,戴冠中,金德琨。简化概率数据关联算法[J]。航空学报,1997; 18(1):83-85。Li Jianxun, Jing Zhongliang, Dai Guanzhong, Jin, Dekun. Smplified proba-bilistic data association algorithm [J]. ACTA AERONAUTICA ET ASTRO-NAUTICA SINICA; 1997, 18(1): 83-85.
[73] 郭晶,罗朋飞,汪浩。密集杂波环境下的数据关联快速算法[J]。航空学报,1998,19(3):305-309。Guo Jing, Luo Pengfei, Wang Hao. Fast algorithm for data association in denseclutter [J]. ACTA AERONAUTICA ET ASTRONAUTICA SINICA, 1998;19(3): 305-309.
[74] James B C. Convergence theory for fuzzy C-means: counter examples andrepairs [J]. IEEE Transactions on Systems, Man and Cybernetics, 1987; 17(5):873-877.
[75] Ashraf M. Aziz, Murali Tunmala, Roberto Cristi. Fuzzy logic data correlationapproach in multi-sensor multi-target tracking systems [J]. Signal Processing,1999; 76: 195-209.
[76] 苏理云,吴钦章,高晓东,何小勇,侯明亮。基于模糊聚类的光电经纬仪多子弹弹道测量。光电工程,2006,33(10):5-8。
[77] SU Li-yun, MA Hong, TANG Shi-fu. Adaptive Image Digital Watermarkingwith DCT and FCM. Wuhan University Journal of Natural Sciences. 2006,11(6): 1657-1660.
[78] 苏理云,唐世福,马洪。基于分形调制阵列的数字水印算法。四川大学学报(自然科学版),2004,41(4):704-707。
[79] 唐世福,苏理云,马洪等。基于混沌置乱的DCT域彩色图像自适应水印算法。四川大学学报(自然科学版),2004,41(2):256-261。
[80] 侯明亮,吴钦章,刘玉然,苏理云。光电跟踪设备智能故障诊断专家系统研究。光电工程,2005,32(S2):5-9。
[81] SU Li-yun, MA Hong, TANG Shi-fu, LI Zheng. Probabilistic Multi-User De-tection on HMM in UWB Systems. 2006 IEEE International Conference onCommnunication Technology, 2006:798-801.
[82] 苏理云。基于分形调制阵列的数字水印算法。成都:四川大学硕士学位论文,2004。
[83] 苏理云,马洪,唐世福。用混沌振子和神经网络检测混沌背景中的弱信号。2006年控制与决策学术年会论文集,2006:259-264。
[84] SU Li-yun, MA Hong,TANG Shi-fu. A Novel Blind Multi-User Detection onKalman Filter in UWB Systems. 第十一届全国青年通信学术年会,2006:12-17。
[85] 徐凤,苏理云,马洪.基于小波变换和模糊聚类的自适应水印嵌入方法. 四川大学学报(自然科学版),2006, 43(5):967-971.
[86] TANG Shi-fu, MA Hong, SU Li-yun. Filter Principle of Hilbert-Huang Trans-form and Its Application in Time Series Analysis. 2006 IEEE InternationalConference on Signal Processing. 2006: 3130-3133.
[87] TANG S F, MA H, SONG E B, SU Li-yun. An Adaptive Blind Scheme forReadable Watermark Based on ALE. 2006 IEEE International Conference onSignal Processing. 2006: 2982-2986.
[88] 何小勇,吴钦章,高晓东,苏理云。光电经纬仪子母弹多子弹轨迹交会法。光电工程,2006,33(9):15-18。
[89] LI Zheng, SU Li-yun, JU Sheng-gen, YANG Jian, Semi-blind Defocus ImageRestoration using BP Neural Network in DWT Domain. Journal of sichuanuniversity (natural science edition),2007:44(1):47-53。
[90] 苏理云,马洪,唐世福。用混沌振子和Kalman滤波检测强分形噪声中的弱信号。四川大学学报(工程科学版),2007,39(3):149-154。
[91] 唐世福,马洪,苏理云。基于分集与谱线增强器的彩色可读水印盲算法。计算机工程,2007,33(7):18-20。
[92] 唐世福,马洪,苏理云。彩色图像有意义水印RAKE接收机的设计。计算机工程,2007,33(7):135-138。
[93] 关新平,范正平,陈彩莲,华长春。混沌控制及其在保密通信中的应用,国防工业出版社,北京,2002。
[94] 张颖,刘艳秋。软计算方法,科学出版社,北京,2002。
[95] 吕金虎,陆君安,陈士华。混沌时间序列分析及其应用,武汉大学出版社,武汉,2002。
[96] 苏理云,庞武。超宽带无线技术—短距离无线通信的前沿技术。技术与市场,2005,5,35。
[97] 苏理云,何小勇。UWB无线通信技术研究。2005四川省通信学会年会论文集,216-219。
[2] G. A. Hirchoren, C. E. D’Alttellis. Estimation of Fractional Brownian Motionwith Multiresolution Kalman Filter Banks[J]. IEEE Trans. on Signal Process-ing, Vol. 47, No. 5, May 1999, pp. 1431-1434.
[3] Gustavo A. Hirchoren, Carlos E. D’Attellis. Estimation of Fractal Signals Us-ing Wavelets and Filter Banks[J]. IEEE Trans. on Signal Processing, Vol. 46,No. 6, June 1998, pp. 1624-1630.
[4] Lei Zhang, Paul Bao, Xiaolin Wu. Wavelet Estimation of Fractional BrownianMotion Embedded in a Noisy Environment[J]. IEEE Trans. on InformationTheory, Vol. 50, No. 9, September, 2004, pp. 2194-2200.
[5] G. W. Wornell, A. V. Oppenheim. Estimation of Fractal Systems from NoisyMeasurements Using Wavelets[J]. IEEE. Trans. on Signal Processing, 1992,40(3):611-622.
[6] 曹坤勇,于盛林。基于正交小波变换的噪声下1/f类分形信号的恢复[J]。南京航空航天大学学报,2004,36(2):245-248。CAO Kun-yong, YU Sheng-lin. Restoration of Noisy 1/f-Type Fractal SignalBased on Orthonormal Wavelet Transformation[J]. Journal of Nanjing Univer-sity of Aeronautics & Astronautics, Vol. 36, No. 2, April, 2004, pp. 245-248.
[7] Shoumin Liu, Zhi Tian. A Kalman-PDA Approach to Soft-Decision Equal-ization for Frequency-Selective MIMO Channels[J]. IEEE Trans. on SignalProcessing, Vol. 53, No. 10, October, 2005, pp. 3819-3830.
[8] Ning Ma, Martin Bouchard, Rafik A. Goubran. Speech Enhancement Usinga Masking Threshold Constrained Kalman Filter and Its Heuristic Implemen-tations[J]. IEEE Trans. on Audio, Speech, and Language Processing, Vol. 14,No. 1, January, 2006, pp. 19-32.
[9] Guangyu Wang, Sailing He. A quantitative study on detection and estimationof weak signals by using chaotic Duffing oscillators [J]. IEEE Trans. on Cir-cuits and Systems-I: Fundamental Theory and applications, 2003, 50(7): 945-953.
[10] 李月,杨宝俊,林洪波,刘晓华。基于特定混沌系统微弱谐波信号频率检测的理论分析与仿真[J]。物理学报,2005,54(5):1994-1999。Li Yue, Yang Bao-Jun, Lin Hong-Bo, et al. Simulation and theoretical analysison detection of the frequency of weak harmonic signals based on a specialchaotic system [J]. ACTA PHYSICA SINICA, 2005,54(5): 1994-1999.
[11] 尚秋峰,尹成群,李士林等。基于Duffing振子的微弱正弦信号检测方法研究[J]。中国电机工程学报,2005,25(2):66-70。SHANGQiu-feng, YIN Cheng-qun, LI Shi-lin, et al. Study on detection ofweak sinusoidal signal by using Duffing oscillator [J]. Proceedings of theCSEE, 2005,25(2):66-70.
[12] P. Flandrin. Wavelet Analysis and synthesis of fractional Brownian Motion[J].IEEE Trans. on Information Theory, 1992, 38(2):910-917.
[13] 王冠宇,陈大军,林建亚等。Duffing振子微弱信号检测方法的统计特性研究[J]。电子学报,1998,26(10):38-44。Guangyu Wang, Dajun Chen, Jingya Lin et al. The statistical characteristics ofweak signal detection based on Duffing oscillator [J]. 1998,26(10): 38-44.
[14] 李月,杨宝俊,石要武。色噪声背景下微弱正弦信号的混沌检测[J]。物理学报,2003,52(3):526-530。Li Yue, Yang Bao-Jun, Shi Yao-Wu. Chaos-based weak sinusoidal signaldetection approach under colored noise background [J]. ACTA PHYSICASINICA, 2003,52(3): 526-530.
[15] G. Wornell. Et al. ,Wavelet based representation for a class of self-similarsignals with application to fractal modulation, IEEE Trans. ,IT-38(2),785800,1992
[16] Neil F Johnson, An Introduction to Watermark Recovery from Images[A], bySANS Intrusion Detection and Response(ID’99)[C], San Diego, CA.:ID,1999
[17] 李世雄,小波变换及其应用,高等教育出版社,1997
[18] 罗建书,小波分析及其应用[M],电子工业出版社,1992
[19] 彭玉华,小波变换与工程应用[M],科学出版社,1999
[20] 杨福生,小波变换的工程分析与应用[M],科学出版社,2000
[21] 董连科,分形理论及其应用,辽宁出版社,1991
[22] 李水根,吴纪桃,分形与小波,科学出版社,2002
[23] 陈守吉,张立明,分形与图像压缩,上海科技教育出版社,1998
[24] 毛用才,胡奇英,随机过程[M],西安电子科技大学出版社,1999
[25] 汪荣鑫,随机过程[M],西安交通大学出版社,1988
[26] 王衍波,薛通,应用密码学,机械工业出版社,2003
[27] Simon Haykin, Sadasivan Puthusserypady. Chaotic Dynamics of Sea Clutter[M]. John Wiley & Sons, Inc, 1999.
[28] Simon Haykon, Jose Principe. Using neural networks to dynamically modelchaotic events such as sea clutter [J]. IEEE Singal Processing Magazine, May,1998, 15(66): 81.
[29] He Jiang-hua, Yang Zhong-kai, Wang Shu. Transient signal detection based onchaos and neural network [J]. ACTA ELECTRONICA SINICA, 1998, 26(10):33-37.何 建 华。 基 于 混 沌 和 神 经 网 络 的 弱 信 号 探 测[J]。 电 子 学报,1998,26(10):33-37。
[30] Guangyu Wang, Dajun Chen, Jingya Lin et al. The statistical characteristics ofweak signal detection based on Duffing oscillator [J]. 1998,26(10): 38-44.王冠宇,陈大军,林建亚等。Duffing振子微弱信号检测方法的统计特性研究[J]。电子学报,1998,26(10):38-44。
[31] Guangyu Wang, Dajun Chen, Jingya Lin et al. The application of chaotic os-cillators to weak signal detection [J]. IEEE Trans. on Industrial Electronics,1999, 46(2): 440-444.
[32] Guangyu Wang, Sailing He. A quantitative study on detection and estimationof weak signals by using chaotic Duffing oscillators [J]. IEEE Trans. on Cir-cuits and Systems-I: Fundamental Theory and applications, 2003, 50(7): 945-953.
[33] Li Yue, Yang Bao-Jun, Shi Yao-Wu. Chaos-based weak sinusoidal signaldetection approach under colored noise background [J]. ACTA PHYSICASINICA, 2003,52(3): 526-530.李月,杨宝俊,石要武。色噪声背景下微弱正弦信号的混沌检测[J]。物理学报,2003,52(3):526-530。
[34] Li Yue, Yang Bao-Jun, Lin Hong-Bo, et al. Simulation and theoretical analysison detection of the frequency of weak harmonic signals based on a specialchaotic system [J]. ACTA PHYSICA SINICA, 2005,54(5): 1994-1999.李月,杨宝俊,林洪波,刘晓华。基于特定混沌系统微弱谐波信号频率检测的理论分析与仿真[J]。物理学报,2005,54(5):1994-1999。
[35] Leung H, Wang S. C. Prediction of noisy chaotic time series using an optimalradial basis function neural network [J]. IEEE Trans. Neural Networks, 2001,12(25): 1163-1172.
[36] Schilling R. J., Carroll J. J. and AL-Ajlouni A. F. Approximation of nonlinearsystems with radial basis function neural networks [J]. IEEE Trans. NeuralNetworks, 2001,12(1): 1-14.
[37] SHANGQiu-feng, YIN Cheng-qun, LI Shi-lin, et al. Study on detection ofweak sinusoidal signal by using Duffing oscillator [J]. Proceedings of theCSEE, 2005,25(2):66-70.尚秋峰,尹成群,李士林等。基于Duffing振子的微弱正弦信号检测方法研究[J]。中国电机工程学报,2005,25(2):66-70。
[38] LU Peng, LI Yue. A modified chaos-based weak sinusoidal signal amplitudedetection approach [J]. ACTA ELECTRONICA SINICA , 2005, 33(3): 527-529.路鹏,李月。微弱正弦信号幅值混沌检测的一种改进方案[J]。电子学报,2005,33(3):527-529。
[39] Jiang Zheng-xin, Shi Guo-liang. Matrix theory and its application [M], Beijingaeronautic and astronautic university press, Beijing, 1988.将正新,施国梁,矩阵理论及其应用[M],北京航空学院出版社,北京,1988。
[40] Cheng Yun-peng. Matrix theory (the second edition) [M], Northwest industryuniversity press, Shanxi, 2002.程云鹏,矩阵论(第二版)[M],西北工业大学出版社,陕西,2002。
[41] M. Z. Win and R. A. Scholtz. Ultra-Wide Bandwidth Time-Hopping Spread-Spectrum Impulse Radio for Wireless Multiple-Access Communications.IEEE Trans. Comm.. Vol.48. No.4, April 2002, pp.679-691.
[42] L. Fullerton, UWB waveforms and coding for communications and radar, Proc.IEEE Tele-systems Conf. , Mar. 26-27, 1991, pp. 139-141.
[43] M. Z. Win and R. A. Scholtz. On the robustness of ultra-wideband width sig-nals in dense multi-path environments, IEEE Communication letters, vol.2, pp.51-53, Feb. 1998.
[44] M. Z. Win and R. A. Scholtz. On the energy capture of ultra-wideband widthsignals in dense multi-path environments, IEEE Communication letters, vol.2,pp. 245-247, Sept. 1998.
[45] F. Ramirez-Mirels, On the performance of ultra-wideband signals in gaussianand dense multipath, IEEE Trans. Vehicular Tech., vol. 2, pp. 244-249, Jan.2001.
[46] M. Hamalainen, V. Hovinen, et al, On the UWB System Coexistence withGSM900, UMTS/WCDMA and GPS, IEEE Journal Select Areas Commun.,Vol.20, Dec. 2002, pp.1712-1721.
[47] R. A. Scholtz. Multiple Access with Time-Hopping Impulse Modulation, Proc.MILCOM’97, Oct. 1993, pp.447-450.
[48] M. Z. Win and R. A. Scholtz, Impulse Radio: How it Work, IEEE Commun.Letters, Vol.2, No.2, Feb. 1998, pp.36-38.
[49] M. Ghosh and C. L. Weber, Maximum likelihood blind equalization, in Proc.SPIE Adaptive Signal Process., 1991, vol. 1565, pp. 188-195.
[50] J. A. R. Fonollosa and J. Vidal, Application of hidden Markov models to blindchannel characterization and data detection, in Proc. IEEE Int. Conf. Acoust.,Speech, Signal process., April. 1994, pp. 185-188.
[51] M. Erkurt and J. G. Proakis, Joint data detection and channel estimation forrapidly fading channels, in Proc. IEEE Global Telecommn. Conf. 1992, pp.910-914.
[52] Carles A H, Fonolloa J A R, Zvonar Z. Probabilistic algorithms for blind adap-tive multiuser detection [J],IEEE Trans on Signal Processing, 1988,46(11), pp:2593-2966.
[53] Rabiner L E, Tutorial of Hidden Markov Models and Selected Applications onSpeech recognition [J], Proceedings of the IEEE, 1989,77 (2), pp: 257-286.
[54] 秦前清,杨宗凯,实用小波分析,西安电子科技大学出版社,1995
[55] P. P. A. Storms, F. C. R. Spieksma. An LP-based Algorithm for the Data Asso-ciation Problem in Multitarget Tracking[J]. 2003, 30: 1067-1085.
[56] Young C. Yoon, Ryuji Kohno, ”Optimum Multi-User Detection in Ultra-Wideband (UWB) Multiple-Access Communication Systems ”, Communi-caitons, 2002. ICC 2002. IEEE International Conference on, Volume:2,28April-2 May 2002, pp:812-816.
[57] Qinghua Li, and Leslie A. Rusch, Multi-user Detection for DS-CDMA UWBin the Home Environment IEEE Journal on Selected Areas in Communications,Vol.20, No.9, Dec. 2002.
[58] Ali Muqaibl, Brian Woerner and Sedki Riad, Application of Multiuser De-tection Techniques to Impulse Radio Time Hopping Multiple Access System,Ultra Wideband Systems and Technologies, 2002,Digest of Papers. 2002 IEEEConference on 2002.
[59] Arslan,H. multiaccess interference cancellation receiver for time-hopping ultrawideband communication, 2004 IEEE International Conference on ,Vol.6,20-24,June 2004,pp:3394-3398.
[60] Ping Liu, Zhengyuan Xu, Jin Tang, Minimum variance multiuser detection forimpulse radio UWB systems, Ultra wideband systems and technologies 2003IEEE international conference on , Nov.16-19,2003,pp:111-115.
[61] Le Martret, C. J, Giannnakis G. B. All digital impulse radio for MUI/ISI re-silient multiuser communications over frequency-selective multipath channels,MILCOM 2000,21st century Military Communications Conference Proceed-ings, vol. 2.
[62] Christophe J. Le Martret , C. J, Giannakis, G. B. all digital impulse radio withmultiuser detection for wireless cellular systems communications, IEEE Trans.on Vol.50 ,Issue.9, Sept. 2002.
[63] Le Martret, C. J, Giannakis, G. B. all digital PPM impulse radio for multiple-access through frequency-selective multipath sensor array and multichannelsignal processing workshop. 2000.Proceedings of the 2000 IEEE.
[64] James B C. Convergence theory for fuzzy C-means: counter examples andrepairs [J]. IEEE Transactions on Systems, Man and Cybernetics, 1987; 17(5):873-877.
[65] 严守峰,吕凤海,柳重堪. 基于最小二乘估计的电视经纬仪交会测量算法[J]. 北京航空航天大学学报,1998,24(5):588-591.YAN Shou-feng, LU Feng-hai, LIU Zhong-kan. Algorithm of movie transitintersection survey via the least square estimation [J]. Journal of Beijing Uni-versity of Aeronautics and Astronautics,1998,24(5):588-591.
[66] 杨 祖 泽. 双 点 后 方 交 会 的 数 学 模 型 及 解 算 程 序[J]. 四 川 测绘,1995,18(4):179-181.Yang Zu-ze. Mathematic Model and Computing Programmer forBackwards Double Ways Intersection [J]. Surveying and Mapping ofSichuan,1995,18(4):179-181.
[67] 柴饶军,纪大山,马彩文. 电视经纬仪复杂多目标交会测量点匹配算法[J].光电工程,2004,31(9):29-32.CHAI Rao-jun,JI Da-shan,MA Cai-wen. Points matching algorithm for so-phisticated multi-target intersection of TV theodolite [J]. Opto-Electronic En-gineering,2004,31(9):29-32.
[68] 廖传锦,黄席樾,柴毅。基于多传感器信息融合的目标跟踪与防撞决策[J]。控制理论与应用,2005; 22(1):127-133。LIAO Chuan-jin, HUANG Xi-yue, CHAI Yi. Target tracking and decision-making for collision avoidance based on multisensor fusion [J]. Control Theory& Applications, 2005; 22(1): 127-133.
[69] Blackman, S. S. Multiple target tracking with radar applications [M]. Boston:Artech House, 1986.
[70] Bar-Shalom. Y. Fortmann. T. E. Tracking and data association [M]. San Diego,CA: Academic Press, 1988.
[71] Bar-Shalom. Y. Multi-target multi-sensor tracking. Principles and Techniques[M]. New York: YBS Publishing, 1995.
[72] 李建勋,敬忠良,戴冠中,金德琨。简化概率数据关联算法[J]。航空学报,1997; 18(1):83-85。Li Jianxun, Jing Zhongliang, Dai Guanzhong, Jin, Dekun. Smplified proba-bilistic data association algorithm [J]. ACTA AERONAUTICA ET ASTRO-NAUTICA SINICA; 1997, 18(1): 83-85.
[73] 郭晶,罗朋飞,汪浩。密集杂波环境下的数据关联快速算法[J]。航空学报,1998,19(3):305-309。Guo Jing, Luo Pengfei, Wang Hao. Fast algorithm for data association in denseclutter [J]. ACTA AERONAUTICA ET ASTRONAUTICA SINICA, 1998;19(3): 305-309.
[74] James B C. Convergence theory for fuzzy C-means: counter examples andrepairs [J]. IEEE Transactions on Systems, Man and Cybernetics, 1987; 17(5):873-877.
[75] Ashraf M. Aziz, Murali Tunmala, Roberto Cristi. Fuzzy logic data correlationapproach in multi-sensor multi-target tracking systems [J]. Signal Processing,1999; 76: 195-209.
[76] 苏理云,吴钦章,高晓东,何小勇,侯明亮。基于模糊聚类的光电经纬仪多子弹弹道测量。光电工程,2006,33(10):5-8。
[77] SU Li-yun, MA Hong, TANG Shi-fu. Adaptive Image Digital Watermarkingwith DCT and FCM. Wuhan University Journal of Natural Sciences. 2006,11(6): 1657-1660.
[78] 苏理云,唐世福,马洪。基于分形调制阵列的数字水印算法。四川大学学报(自然科学版),2004,41(4):704-707。
[79] 唐世福,苏理云,马洪等。基于混沌置乱的DCT域彩色图像自适应水印算法。四川大学学报(自然科学版),2004,41(2):256-261。
[80] 侯明亮,吴钦章,刘玉然,苏理云。光电跟踪设备智能故障诊断专家系统研究。光电工程,2005,32(S2):5-9。
[81] SU Li-yun, MA Hong, TANG Shi-fu, LI Zheng. Probabilistic Multi-User De-tection on HMM in UWB Systems. 2006 IEEE International Conference onCommnunication Technology, 2006:798-801.
[82] 苏理云。基于分形调制阵列的数字水印算法。成都:四川大学硕士学位论文,2004。
[83] 苏理云,马洪,唐世福。用混沌振子和神经网络检测混沌背景中的弱信号。2006年控制与决策学术年会论文集,2006:259-264。
[84] SU Li-yun, MA Hong,TANG Shi-fu. A Novel Blind Multi-User Detection onKalman Filter in UWB Systems. 第十一届全国青年通信学术年会,2006:12-17。
[85] 徐凤,苏理云,马洪.基于小波变换和模糊聚类的自适应水印嵌入方法. 四川大学学报(自然科学版),2006, 43(5):967-971.
[86] TANG Shi-fu, MA Hong, SU Li-yun. Filter Principle of Hilbert-Huang Trans-form and Its Application in Time Series Analysis. 2006 IEEE InternationalConference on Signal Processing. 2006: 3130-3133.
[87] TANG S F, MA H, SONG E B, SU Li-yun. An Adaptive Blind Scheme forReadable Watermark Based on ALE. 2006 IEEE International Conference onSignal Processing. 2006: 2982-2986.
[88] 何小勇,吴钦章,高晓东,苏理云。光电经纬仪子母弹多子弹轨迹交会法。光电工程,2006,33(9):15-18。
[89] LI Zheng, SU Li-yun, JU Sheng-gen, YANG Jian, Semi-blind Defocus ImageRestoration using BP Neural Network in DWT Domain. Journal of sichuanuniversity (natural science edition),2007:44(1):47-53。
[90] 苏理云,马洪,唐世福。用混沌振子和Kalman滤波检测强分形噪声中的弱信号。四川大学学报(工程科学版),2007,39(3):149-154。
[91] 唐世福,马洪,苏理云。基于分集与谱线增强器的彩色可读水印盲算法。计算机工程,2007,33(7):18-20。
[92] 唐世福,马洪,苏理云。彩色图像有意义水印RAKE接收机的设计。计算机工程,2007,33(7):135-138。
[93] 关新平,范正平,陈彩莲,华长春。混沌控制及其在保密通信中的应用,国防工业出版社,北京,2002。
[94] 张颖,刘艳秋。软计算方法,科学出版社,北京,2002。
[95] 吕金虎,陆君安,陈士华。混沌时间序列分析及其应用,武汉大学出版社,武汉,2002。
[96] 苏理云,庞武。超宽带无线技术—短距离无线通信的前沿技术。技术与市场,2005,5,35。
[97] 苏理云,何小勇。UWB无线通信技术研究。2005四川省通信学会年会论文集,216-219。