本课程是一门以介绍现代信号处理技术的技术专业课。现代信号处理着重于有限数据量的随机信号的分析与处理，它是近代才发展起来的前沿学科，它的出现改变了人们对信号的认识，拓宽了信号处理研究的领域，发展了一系列新的信号分析理论，如现代谱估计（ARMA参数估计、高阶谱估计）、同态分析、时频分析（短时傅立叶变换、Gabor变换、小波变换、Randon－Wigner变换、分数阶傅立叶变换、线调频小波变换）、循环平稳信号处理等。本课程的任务是通过教学各个环节，运用各种教学手段和方法，使学生掌握现代信号处理中的基本理论和基本计算方法；培养学生提高信号处理建模的能力，并通过该课程的学习扩大知识面，为今后的研究和技术工作打下坚实的基础。

Course Contents and Outline:Chap. 1 Basics of Discrete-Time Signal1.1Mathematic Representation and Basic Characteristics of Random Signals1.2Basic Models of Discrete-Time Signal1.3Basics of Estimation Theory1.4Basic Estimation MethodsChap. 2 Modern Spectrum Analysis2.1Periodic Table and BT Spectrum2.2Levinson-Durbin Recursion2.3AR Spectrum Estimation and Basic Methods of Order Determination2.4Principles of MUSIC and ESPRITChap. 3 Homomorphic Analysis3.1Mathematic Principles3.2Application in Speech Signal ProcessingChap. 4 Multiple Time Series Analysis4.1Second-Order Characteristics of Multiple Time Series4.2Multiple ARMA, AR Process Modeling4.3Self-Adaptive Multichannel Least-Squares Lattice FilterChap. 5 Non-Gaussian Signal Processing5.1Characteristics of Cumulants5.2Cumulants-Based ARMA, AR models5.3Cumulants-Based Self-Adaptive Parameter EstimationChap. 6 Time-Frequency Analysis6.1Basic Theory6.2Short-Time Fourier Transform6.3Short-Time Gabor Transform6.4WV Distribution and the Influences of Cross-Term6.5 Cross-Term Removing6.6 Applications of Time-Frequency AnalysisChap. 7 Wavelet Transform7.1Basic Principles7.2Basics of Discrete Wavelet Transform7.3Frame Theory, Multi-Resolution Analysis and Filter Theory7.4Mallat Algorithm7.5 Construction of Orthogonal Wavelet7.6 Biorthogonal Wavelet and Wavelet PacketChap. 8 Cyclostationary Signal Processing8.1Characteristics of Cyclostationary Signals8.2Estimation of Cyclic Statistics8.3Estimation of Cyclic Spectrum