电子系统中多模噪声的研究
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摘要
信号处理是信息传输科学中近十几年发展最快的学科之一,传统的信号处理有三个基本的假设:线性,高斯性和平稳性;而现代信号处理方法是以非线性,非高斯性和非平稳性作为分析与处理的对象,尤其以带有非高斯噪声的信号处理引人注目。
本文研究是建立在双模噪声基础上的通信和信号处理理论进行了完善和补充。从而研究了多模噪声,实质是多种噪声迭加产生的混合噪声,属于非高斯噪声。非高斯噪声的研究方法一般是高阶统计量法,本文采用了现代信号处理的方法。主要工作如下:
(1)综述了通信中信号处理发展现状,介绍了噪声处理技术历史和发展的现状,并且说明了多模噪声研究的理论意义。
(2)系统地研究和论述了各种非高斯噪声理论,然后对不同类型的噪声信号进行了分析,实现有用信号和噪声分离。系统地阐述了统计信号检测基本理论和统计信号的判决准则。
(3)研究和分析了双模噪声模型,引出了多模混合噪声的四种模型,深入分析了多模噪声的统计特性中绝对值均值和平均功率,为以后的研究提供了基础。
(4)研究了自适应算法和LMS Newton算法,提出了改进算法,使得噪声和信号很好的分离。
(5)研究了基于贝叶斯理论的信号估计,在实际问题中,利用带有噪声的观测量对系统状态进行滤波与估计,常常采用状态空间法对系统建模。本文提出了一种结合多层感知器(MLP)和粒子滤波融合算法。
In the past decade, the processing of signal has been one of the fastest developing subjects. The traditional signal processing is based on three suppose: linearity, Gaussianity and stationarity. While the modern signal processing non-linearity, non-Gaussianity and non-stationarity. Especially, the signal with the non-Gaussian noise has caught most people's eyes.
This paper gives some perfection and complement to the theory of communication & signal processing based on bimodal hybrid noise. Then multi-modal noise has been propose, and is a hybrid noise consisting of many kinds of additive noises. It belongs to non-Gaussian noise. Generally,the method used to study non-Gaussian noise is using high-order statistics(HOS). The paper uses the method of modern signal processing. Main content of this paper is as follows:
(1)It reviews present conditions of development of communication systems, and introduces the history and current situation of the processing technology of noise, and provides the perspective of the theory of multi-modal noise.
(2)The thesis systematically studied and discussed a variety of non-Gaussian noise theory. Then illustrate all kinds of noise and put forward exact algorithm to separate the noise and signal. We also systematically described the basic theory of statistical signal detection and the adjudging rules of statistical signal.
(3) Research and analysis of the bimodal noise, leads to the four models of the multi-modal hybrid noise. Then in-depth analyze the statistical properties of multi-modal noise in the absolute mean and average power. So it is the foundation for future research.
(4)Research of the adaptive algorithm and LMS Newton algorithm, and the improved algorithm has been proposed, which is making the noise and signal well separated.
(5)It studies estimation based on Bayesian theory, in many problems, used observed value with noise to filter and estimate on systems status, people often adopt state space method to simulated system. The paper provides a kind of particle filter with MLP fusion to realize to signal estimation.
本文研究是建立在双模噪声基础上的通信和信号处理理论进行了完善和补充。从而研究了多模噪声,实质是多种噪声迭加产生的混合噪声,属于非高斯噪声。非高斯噪声的研究方法一般是高阶统计量法,本文采用了现代信号处理的方法。主要工作如下:
(1)综述了通信中信号处理发展现状,介绍了噪声处理技术历史和发展的现状,并且说明了多模噪声研究的理论意义。
(2)系统地研究和论述了各种非高斯噪声理论,然后对不同类型的噪声信号进行了分析,实现有用信号和噪声分离。系统地阐述了统计信号检测基本理论和统计信号的判决准则。
(3)研究和分析了双模噪声模型,引出了多模混合噪声的四种模型,深入分析了多模噪声的统计特性中绝对值均值和平均功率,为以后的研究提供了基础。
(4)研究了自适应算法和LMS Newton算法,提出了改进算法,使得噪声和信号很好的分离。
(5)研究了基于贝叶斯理论的信号估计,在实际问题中,利用带有噪声的观测量对系统状态进行滤波与估计,常常采用状态空间法对系统建模。本文提出了一种结合多层感知器(MLP)和粒子滤波融合算法。
In the past decade, the processing of signal has been one of the fastest developing subjects. The traditional signal processing is based on three suppose: linearity, Gaussianity and stationarity. While the modern signal processing non-linearity, non-Gaussianity and non-stationarity. Especially, the signal with the non-Gaussian noise has caught most people's eyes.
This paper gives some perfection and complement to the theory of communication & signal processing based on bimodal hybrid noise. Then multi-modal noise has been propose, and is a hybrid noise consisting of many kinds of additive noises. It belongs to non-Gaussian noise. Generally,the method used to study non-Gaussian noise is using high-order statistics(HOS). The paper uses the method of modern signal processing. Main content of this paper is as follows:
(1)It reviews present conditions of development of communication systems, and introduces the history and current situation of the processing technology of noise, and provides the perspective of the theory of multi-modal noise.
(2)The thesis systematically studied and discussed a variety of non-Gaussian noise theory. Then illustrate all kinds of noise and put forward exact algorithm to separate the noise and signal. We also systematically described the basic theory of statistical signal detection and the adjudging rules of statistical signal.
(3) Research and analysis of the bimodal noise, leads to the four models of the multi-modal hybrid noise. Then in-depth analyze the statistical properties of multi-modal noise in the absolute mean and average power. So it is the foundation for future research.
(4)Research of the adaptive algorithm and LMS Newton algorithm, and the improved algorithm has been proposed, which is making the noise and signal well separated.
(5)It studies estimation based on Bayesian theory, in many problems, used observed value with noise to filter and estimate on systems status, people often adopt state space method to simulated system. The paper provides a kind of particle filter with MLP fusion to realize to signal estimation.
引文
[1]张贤达,保铮.非平稳信号分析与处理[M ],北京:国防工业出版社,1998.
[2] Martin R D, Schiwartz S .C. Robust detection of a known signal in early Gaussian noise [J]. IEEE Trans on IT,1 971:JT-17(1):50-56.
[3] Simon Haykin. Communication Systems[M].Fourth Edition.北京:电子工业出版社,2003.
[4]樊昌信.通信原理[M].第四版.北京:国防工业出版社,1995.
[5]曹志刚,钱亚生.现代通信原理[M].北京:清华大学出版社,1992.
[6]罗鹏飞等.统计信号处理基础─估计与检测理论[M].北京:电子工业出版社,2003.
[7]申丽然.基于听觉外周模型和高阶统计量语音流检测[D].哈尔滨:哈尔滨工程大学硕士论文2003,5-10
[8]林佳仕.短波通信中信号检测算法研究[D].哈尔滨:哈尔滨工程大学硕士论文2004 ,5-8
[9]贺涛.数字通信信号调制识别若干新问题研究[D].成都:电子科技大学博士论文2007 ,58-60
[10] M.D.斯里纳思(Srinath)、P.K.雷杰斯卡兰(Rajasekaran),统计信号处理及其应用导论(An introduction to statistical signal processing with applications)[M],国防工业出版社,1982年,180-250
[11] A.P.塞奇(Sage)、J.L.梅尔萨(Melsa).估计理论及其在通讯与控制中的应用(Estimation theory with applications to communications and control)[M],科学出版社,1978年.
[12]刘有恒.信号检测与估计[M].人民邮电出版社,1989年.
[13]张树京.统计信号处理[M].人民邮电出版社,1988年.
[14]沈凤麟、钱玉美.信号统计分析基础[M].中国科学技术大学出版社,1989年.
[15]王宏禹.随机数字信号处理[M].科学出版社,1988年.
[16]陈炳和.随机信号处理[M].国防工业出版社,1996年.
[17]黄玉划,山拜.达拉拜.双模噪声中信号的检测[J].电路与系统学报, 2002, 7(1): 37-40
[18]陆传赉.工程系统中的随机过程[M].电子工业出版社,2000年.
[19]朱华.随机信号分析.北京理工大学出版社,1990
[20]黄玉划,山拜·达拉拜.双模噪声中信号识别与精确估计[J].电子测量技术.2002,(2):10-12
[21]山拜·达拉拜,黄玉划.多模噪声理论及其在通信保密中的应用[J].电讯技术,2008,48(2):20-24
[22]戴逸松.微弱信号检测方法及仪器[M].北京:国防工业出版社1994
[23] B.Msadler and G.B.Giannakis. Detection in colored non-Gaussian noise using cumulant [A]. In Proc. IEEE ICASSP93. 1993, 4(5):204-207
[24]张贤达.现代信号处理[M] .北京:清华大学出版社,1995
[25]何建华,杨宗凯,王殊.基于混沌和神经网络的弱信号探测[J].电子学报.1998(10):33-37
[26]叶平贤,龚沈光.舰船物理场[M].北京:兵器工业出版社,1992.
[27] Alou P,Oliver J,Cobos J A,Garcia O.Buck+HaIf-Bridge(d=50%)Topology Applied to very Low Voltage Power Converters[A].IEEE,APEC 2001[C].2001,2:715-721.
[28] Lee Te—Won,Lewicki M S,Sejnowski T J.ICA Mixture Models for Unsupervised Classification of Non—Gaussian Classes & Automatic Context Switching in Blind Signal Separation[J].IEEE Trans on PA&MI,2000,22(10):1078-1089.
[29]高鹰,谢胜利.一种修正LMS牛顿算法[J].电路与系统学报.2004,19(3):80—83.
[30]覃景繁,韦岗.基于S型函数的变步长LMS自适应滤波算法[J].无线电工程,1996,26(4):443-447.
[31] Gorden N J,Salmond D J,Smith AF M. Novel approach to Nonlinear non-Gaussian Bayesian state estimation[J].IEEE Proc Inst ElectEng F.1993.140(2):107-113.
[32] Arnaud Doucet, Simon Godsill, Christophe Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering [J]. Statistics and Computing (S0960-3174), 2000, 10(1): 197-208.
[33] WEI Kun, ZHAO Yong-qiang , PAN Quan , et al. IR target tracking based on mean shift and particle filter[J] . Journal of Optoelectronics·Laser(光电子·激光) ,2008 ,19(2) :213-214. (in Chinese)
[34] Arulamppalam M S, Maskell S, Gordon N J. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].IEEE Trans on Signal Processing, 2002,50(2):174-188.
[35] Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering [J] . Statistics and Computing. 2000 .10 (3) :197-208
[36] Cappe O. Godsill S. J., and Moulines E. An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo [J], Proceedings of the IEEE, 2007.95(5):899-924.
[37] DOUCET A,GORDON N.Sequential Monte Carlo Methods in Practice [M].New York:Springer-Verlag,2001.
[38]边肇祺,张学工.模式识别[M].北京:清华大学出版社,1999.
[39] Simon Haykin.自适应滤波器原理[M].郑宝玉,等译。4版.北京:电子工业出版社,2003:183-267.
[40]梁军.粒子滤波算法及其应用研究[D].哈尔滨:哈尔滨工业大学博士论文2007 ,20-22
[41] P.D.Moral. Measure Valued Processes and Interacting Particle Systems. Application to Nonlinear Filtering Problems [J].Ann. Appl. Probab.1998.8 (2):438-495.
[42]C.Zhe. Bayesian Filtering: From Kalman Filters to Particle Filters and Beyond [D]. Hamilton: McMaster University.2003.
[43]边肇棋等.模式识别[M].北京:清华大学出版社.1988
[44] A.K. Jain, Robert P.W. Durbin,J.C. Mao, Statistical Patter Recognition: A Review [J].IEEE Trans. PAMI,2000,22(1):4-37
[2] Martin R D, Schiwartz S .C. Robust detection of a known signal in early Gaussian noise [J]. IEEE Trans on IT,1 971:JT-17(1):50-56.
[3] Simon Haykin. Communication Systems[M].Fourth Edition.北京:电子工业出版社,2003.
[4]樊昌信.通信原理[M].第四版.北京:国防工业出版社,1995.
[5]曹志刚,钱亚生.现代通信原理[M].北京:清华大学出版社,1992.
[6]罗鹏飞等.统计信号处理基础─估计与检测理论[M].北京:电子工业出版社,2003.
[7]申丽然.基于听觉外周模型和高阶统计量语音流检测[D].哈尔滨:哈尔滨工程大学硕士论文2003,5-10
[8]林佳仕.短波通信中信号检测算法研究[D].哈尔滨:哈尔滨工程大学硕士论文2004 ,5-8
[9]贺涛.数字通信信号调制识别若干新问题研究[D].成都:电子科技大学博士论文2007 ,58-60
[10] M.D.斯里纳思(Srinath)、P.K.雷杰斯卡兰(Rajasekaran),统计信号处理及其应用导论(An introduction to statistical signal processing with applications)[M],国防工业出版社,1982年,180-250
[11] A.P.塞奇(Sage)、J.L.梅尔萨(Melsa).估计理论及其在通讯与控制中的应用(Estimation theory with applications to communications and control)[M],科学出版社,1978年.
[12]刘有恒.信号检测与估计[M].人民邮电出版社,1989年.
[13]张树京.统计信号处理[M].人民邮电出版社,1988年.
[14]沈凤麟、钱玉美.信号统计分析基础[M].中国科学技术大学出版社,1989年.
[15]王宏禹.随机数字信号处理[M].科学出版社,1988年.
[16]陈炳和.随机信号处理[M].国防工业出版社,1996年.
[17]黄玉划,山拜.达拉拜.双模噪声中信号的检测[J].电路与系统学报, 2002, 7(1): 37-40
[18]陆传赉.工程系统中的随机过程[M].电子工业出版社,2000年.
[19]朱华.随机信号分析.北京理工大学出版社,1990
[20]黄玉划,山拜·达拉拜.双模噪声中信号识别与精确估计[J].电子测量技术.2002,(2):10-12
[21]山拜·达拉拜,黄玉划.多模噪声理论及其在通信保密中的应用[J].电讯技术,2008,48(2):20-24
[22]戴逸松.微弱信号检测方法及仪器[M].北京:国防工业出版社1994
[23] B.Msadler and G.B.Giannakis. Detection in colored non-Gaussian noise using cumulant [A]. In Proc. IEEE ICASSP93. 1993, 4(5):204-207
[24]张贤达.现代信号处理[M] .北京:清华大学出版社,1995
[25]何建华,杨宗凯,王殊.基于混沌和神经网络的弱信号探测[J].电子学报.1998(10):33-37
[26]叶平贤,龚沈光.舰船物理场[M].北京:兵器工业出版社,1992.
[27] Alou P,Oliver J,Cobos J A,Garcia O.Buck+HaIf-Bridge(d=50%)Topology Applied to very Low Voltage Power Converters[A].IEEE,APEC 2001[C].2001,2:715-721.
[28] Lee Te—Won,Lewicki M S,Sejnowski T J.ICA Mixture Models for Unsupervised Classification of Non—Gaussian Classes & Automatic Context Switching in Blind Signal Separation[J].IEEE Trans on PA&MI,2000,22(10):1078-1089.
[29]高鹰,谢胜利.一种修正LMS牛顿算法[J].电路与系统学报.2004,19(3):80—83.
[30]覃景繁,韦岗.基于S型函数的变步长LMS自适应滤波算法[J].无线电工程,1996,26(4):443-447.
[31] Gorden N J,Salmond D J,Smith AF M. Novel approach to Nonlinear non-Gaussian Bayesian state estimation[J].IEEE Proc Inst ElectEng F.1993.140(2):107-113.
[32] Arnaud Doucet, Simon Godsill, Christophe Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering [J]. Statistics and Computing (S0960-3174), 2000, 10(1): 197-208.
[33] WEI Kun, ZHAO Yong-qiang , PAN Quan , et al. IR target tracking based on mean shift and particle filter[J] . Journal of Optoelectronics·Laser(光电子·激光) ,2008 ,19(2) :213-214. (in Chinese)
[34] Arulamppalam M S, Maskell S, Gordon N J. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].IEEE Trans on Signal Processing, 2002,50(2):174-188.
[35] Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering [J] . Statistics and Computing. 2000 .10 (3) :197-208
[36] Cappe O. Godsill S. J., and Moulines E. An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo [J], Proceedings of the IEEE, 2007.95(5):899-924.
[37] DOUCET A,GORDON N.Sequential Monte Carlo Methods in Practice [M].New York:Springer-Verlag,2001.
[38]边肇祺,张学工.模式识别[M].北京:清华大学出版社,1999.
[39] Simon Haykin.自适应滤波器原理[M].郑宝玉,等译。4版.北京:电子工业出版社,2003:183-267.
[40]梁军.粒子滤波算法及其应用研究[D].哈尔滨:哈尔滨工业大学博士论文2007 ,20-22
[41] P.D.Moral. Measure Valued Processes and Interacting Particle Systems. Application to Nonlinear Filtering Problems [J].Ann. Appl. Probab.1998.8 (2):438-495.
[42]C.Zhe. Bayesian Filtering: From Kalman Filters to Particle Filters and Beyond [D]. Hamilton: McMaster University.2003.
[43]边肇棋等.模式识别[M].北京:清华大学出版社.1988
[44] A.K. Jain, Robert P.W. Durbin,J.C. Mao, Statistical Patter Recognition: A Review [J].IEEE Trans. PAMI,2000,22(1):4-37