 | 书 名: 数字信号处理原理、算法与应用（第三版·影印版）
作 者: （美）John G.Proakis,Dimitris G.Manolakis
出 版 社: 中国电力出版社
ISBN : 750832499
原 价: ￥89
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目 录 PREFACE 1 INTRODUCTION 1.1 Signals,Systems,and Signal Processing 1.1.1 Basic Elements of a Digital Signal Processing System 1.1.2 Advantages of Digital over Analog Signal Processing 1.2 Classification of Signals 1.2.1 Multichannel and Multidimensional Signals 1.2.2 Continuous-Time Versus Discrete-Time Signals 1.2.3 Continuous-Valued Versus Discrete-Valued Signals 1.2.4 Deterministic Versus Random Signals 1.3 The Concept of Frequency in Continuous-Time and Discrete-Time Signals 1.3.1 Continuous-Time Sinusoidal Signals 1.3.2 Discrete-Time Sinusoidal Signals 1.3.3 Harmonically Related Complex Exponentials 1.4 Analog-to-Digital and Digital-to-Analog Conversion 1.4.1 Sampling of Analog Signals 1.4.2 The Sampling Theorem 1.4.3 Quantization of Continuous-Amplitude Signals 1.4.4 Quantization of Sinusoidal Signals 1.4.5 Coding of Quantized Samples 1.4.6 Digital-to-Analog Conversion 1.4.7 Analysis of Digital Signals and Systems Versus Discrete-Time Signals and Systems 1.5 Summary and References Problems 2 DISCRETE-TIME SIGNALS AND SYSTEMS 2.1 Discrete-Time Signals 2.1.1 Some Elementary Discrete-Time Signals 2.1.2 Classification of Discrete-Time Signals 2.1.3 Simple Manipulations of Discrete-Time Signals 2.2 Discrete-Time Systems 2.2.1 Input-Output DeScription of Systems 2.2.2 Block Diagram Representation of Discrete-Time Systems 2.2.3 Classification of Discrete-Time Systems 2.2.4 Interconnection of Discrete-Time Systems 2.3 Analysis of Discrete-Time Linear Time-Invariant Systems 2.3.1 Techniques for the Analysis of Linear Systems 2.3.2 Resolution of a Discrete-Time Signal into Impulses 2.3.3 Response of LTI Systems to Arbitrary Inputs:The Convolution Sum 2.3.4 Properties of Convolution and the Interconnection of LTl Systems 2.3.5 Causal Linear Time-Invariant Systems 2.3.6 Stability of Linear Time-Invariant Systems 2.3.7 Systems with Finite-Duration and Infinite-Duration Impulse Response 2.4 Discrete-Time Systems Described by Difference Equations 2.4.1 Recursive and Nonrecursive Discrete-Time Systems 2.4.2 Linear Time-Invariant Systems Characterized by Constant-Coefficient Difference Equations 2.4.3 Solution of Linear Constant-Coefficient Difference Equations 2.4.4 The Impulse Response of a Linear Time-Invariant Recursive System 2.5 Implementation of Discrete-Time Systems 2.5.1 Structures for the Realization of Linear Time-Invariant Systems 2.5.2 Recursive and Nonrecumve Realizations of FIR Systems 2.6 Correlation of Discrete-Time Signals 2.6.1 Crosscorrelation and Autocorrelation Sequences 2.6.2 Properties of the Autocorrelation and Crosscorrelation Sequences 2.6.3 Correlation of Periodic Sequences 2.6.4 Computation of Correlation Sequences 2.6.5 Input-Output Correlation Sequences 2.7 Summary andReferences134 Problems135 3 THE Z-TRANSFORM AND ITS APPLICATION TO THEANALYSIS OF LTlSYSTEMS 3.1 The z-Transform 3.1.1 The Direct z-Transform 3.1.2 The Inverse z-Transform 3.2 Properties o fthe z-Transform 3.3 Rational z-Transforms 3.3.1 Poles and Zeros 3.3.2 Pole Location and Time-Domain Behavior for Causal Signals 3.3.3 The System Function of a Linear Time-Invariant System 3.4 Inversion of the z-Transform 3.4.1 The Inversez-Trans form by Contour Integration 3.4.2 The Inversez-Trans form by Power Series Expansion 3.4.3 The Inversez-Trans form by Partial-Fraction Expansion 3.4.4 Decompositi on of Rationalz-Transforms 3.5 The One-sided z-Transform 3.5.1 Definition and Propertie 3.5.2 Solution of Difference Equations 3.6 Analysis of Linear Time-Invariant Systems in the z-Domain 3.6.1 Response of Systems with Rational System Functions 3.6.2 Response of Pole-Zero Systems with Nonzero Initial Conditions 3.6.3 Transient and Steady-State Responses 3.6.4 Causality and Stability 3.6.5 Pole-Zero Cancellations 3.6.6 Multiple-Order Poles and Stability 3.6.7 The Schur-Cohn Stability Test 3.6.8 Stability of Second-Order Systems 3.7 Summary and References Problems 4 FREQUENCY ANALYSIS OF SIGNALS AND SYSTEMS 4.1 Frequency Analysis of Continuous-Time Signals 4.1.1 The Fourier Series for Continuous-Time Periodic Signals 4.1.2 Power Density Spectrum of Periodic Signals 4.1.3 The Fourier Transform for Continuous-Time Aperiodic Signals 4.1.4 Energy Density Spectrum of Aperiodic Signals 4.2 Frequency Analysis of Discrete-Time Signals 4.2.1 The Fourier Series for Discrete-Time Periodic Signals 4.2.2 Power Density Spectrum of Periodic Signals 4.2.3 The Fourier Transform of Discrete-Time Aperiodic Signals 4.2.4 Convergence of the Fourier Transform 4.2.5 Energy Density Spectrum of Aperiodic Signals 4.2.6 Relationship of the Fourier Transform to the z-Transform 4.2.7 The Cepstrum 4.2.8 The Fourier Transform of Signals with Poleson the Unit Circle 4.2.9 The Sampling Theorem Revisited 4.2.10 Frequency-Domain Classification of Signals:The Concept of Bandwidth 4.2.11 The Frequency Ranges of Some Natural Signals 4.2.12 Physical and Mathematical Dualities 4.3 Properties of the Fourier Transform for Discrete-Time Signals 4.3.1 Symmetry Properties of the Fourier Transform 4.3.2 Fourier Transform Theorems and Properties 4.4 Frequency-Domain Characteristics of Linear Time-Invariant Systems 4.4.1 Response to Complex Exponential and Sinusoidal Signals:The Frequency Response Function 4.4.2 Steady-Stateand Transient Response to Sinusoidal Input Signals 4.4.3 Steady-State Response to Periodic Input Signals 4.4.4 Response to Aperiodic Input Signals 4.4.5 Relationships Between the System Function and the Frequency Response Function 4.4.6 Computationof the Frequency Response Function 4.4.7 Input-Output Correlation Functions and Spectra 4.4.8 Correlation Functions and Power Spectra for RandomInput Signals 4.5 Linear Time-Invariant Systems as Frequency-Selective Filters 4.5.1 Ideal Filter Characteristics 4.5.2 Lowpass,Highpass,and Bandpass Filters 4.5.3 Digital Resonators 4.5.4 Notch Filters 4.5.5 Comb Filters 4.5.6 All-Pass Filters 4.5.7 Digital Sinusoidal Oscillators 4.6 Inverse Systems and Deconvolution 4.6.1 Invertibility of Linear Time-Invariant Systems 4.6.2 Minimum-Phase,Maximum-Phase,and Mixed-Phase Systems 4.6.3 System Identification and Deconvolution 4.6.4 Homomorphic Deconvolution Summary and References Problems 5 THE DISCRETE FOURIER TRANSFORM:ITS PROPERTIES AND APPLICATIONS 5.1 Frequency Domain Sampling:The Discrete Fourier Transform 5.1.1 Frequency-Domain Sampling and Reconstruction of Discrete-Time Signals 5.1.2 The Discrete Fourier Transform DFT 5.1.3 The DFT as a Linear Transformation 5.1.4 Relationship of the DFT to Other Transforms 5.2 Properties of the DFT 5.2.1 Periodicity,Linearity,and Symmetry Properties 5.2.2 Multiplication of Two DFTs and Circular Convolution 5.2.3 Additional DFT Properties 5.3 Linear Filtering Methods Basedon the DFT 5.3.1 Use of the DFT in Linear Filtering 5.3.2 Filtering of Long Data Sequences 5.4 Frequency Analysis of Signals Using the DFT 5.5 Summary and References Problems 6 EFFICIENT COMPUTATION OF THE DFT:FAST FOURIER TRANSFORM ALGORITHMS 6.1 Efficient Computation of the DFT:FFT Algorithms 6.1.1 Direct Computation of the DFT 6.1.2 Divide-and-Conquer Approach to Computation of the DFT 6.1.3 Radix-2FFT Algorithms 6.1.4 Radix-4FFT Algorithms 6.1.5 Split-Radix FFT Algorithms 6.1.6 Implementation of FFT Algorithms 6.2 Applications of FFT Algorithms 6.2.1 Efficient Computation of the DFT of Two Real Sequences 6.2.2 Efficient Computation of the DFT of a 2N-Point Real Sequence 6.2.3 Use of the FFT Algorithm in Linear Filtering and Correlation 6.3 A Linear Filtering Approach to Computation of the DFT 6.3.1 The Goertzel Algorithm 6.3.2 The Chirp-z Transform Algorithm 6.4 Quantization Effects in the Computation ofthe DFT 6.4.1 Quantization Errors in the Direct Computation of the DFT 6.4.2 Quantization Errors in FFT Algorithms 6.5 Summary and References Problems 7 IMPLEMENTATION OF DISCRETE-TIME SYSTEMS 7.1 Structures for the Realization of Discrete-Time Systems 7.2 Structures for FIR Systems 7.2.1 Direct-Form Structure 7.2.2 Cascade-Form Structures 7.2.3 Frequency-Sampling Structures 7.2.4 Lattice Structure 7.3 Structures for IIR Systems 7.3.1 Direct-Form Structures 7.3.2 Signal Flow Graphs and Transposed Structures 7.3.3 Cascade-Form Structures 7.3.4 Parallel-Form Structures 7.3.5 Lattice and Lattice-Ladder Structures for IIR Systems 7.4 State-Space System Analysis and Structures 7.4.1 State-Space DeScriptions of Systems Characterized by Difference Equations 7.4.2 Solution of the State-Space Equations 7.4.3 Relationships Between Input-Output and State-Space DeScriptions 7.4.4 State-Space Analysis in the z-Domain 7.4.5 Additional State-Space Structures 7.5 Representation of Numbers 7.5.1 Fixed-Point Representation of Numbers 7.5.2 Binary Floating-Point Representation of Numbers 7.5.3 Errors Resulting from Rounding and Truncation 7.6 Quantization of Filter Coefficients 7.6.1 Analysis of Sensitivity to Quantization of Filter Coefficients 7.6.2 Quantization of Coefficients in FIR Filters 7.7 Round-Off Effects in Digital Filters 7.7.1 Limit-Cycle Oscillations in Recursive Systems 7.7.2 Scaling to Prevent Overflow 7.7.3 Statistical Characterization of Quantization Effects in Fixed-Point Realizations of Digital Filters 7.8 Summary and References Problems 8 DESIGN OF DIGITAL FILTERS 8.1 General Considerations 8.1.1 Causality and Its Implications 8.1.2 Characteristics of Practical Frequency-Selective Filters 8.2 Design of FIR Filters 8.2.1 Symmetric and Antisymmetric FIR Filters 8.2.2 Design of Linear-Phase FIR Filters Using Windows 8.2.3 Design of Linear-Phase FIR Filters by the Frequency-Sampling Method 8.2.4 Design of Optimum Equiripple Linear-Phase FIR Filters 8.2.5 Design of FIR Differentiators 8.2.6 Design of Hilbert Transformers 8.2.7 Comparison of Design Methods for Linear-Phase FIR Filters 8.3 Design of IIR Filters From Analog Filters 8.3.1 IIR Filter Design by Approximation of Derivatives 8.3.2 IIR Filter Design by Impulse Invariance 8.3.3 IIR Filter Design by the Bilinear Transformation 8.3.4 The Matched-z Transformation 8.3.5 Characteristics of Commonly Used Analog Filters 8.3.6 Some Examples of Digital Filter Designs Based on the Bilinear Transformation 8.4 Frequency Transformations 8.4.1 Frequency Transformations in the Analog Domain 8.4.2 Frequency Transformations in the Digital Domain 8.5 Design of Digital Filters Based on Least-Squares Method 8.5.1 Pade Approximation Method 8.5.2 Least-Squares Design Methods 8.5.3 FIR Least-Squares Inverse Wiener Filters 8.5.4 Design of IIR Filters in the Frequency Domain 8.6 Summary and References Problems 9 SAMPLING AND RECONSTRUCTION OF SIGNALS 9.1 Sampling of Bandpass Signals 9.1.1 Representation of Bandpass Signals 9.1.2 Sampling of Bandpass Signals 9.1.3 Discrete-Time Processing of Continuous-Time Signals 9.2 Analog-to-Digital ConverSion 9.2.1 Sample-and-Hold 9.2.2 Quantization and Coding 9.2.3 Analysis of Quantization Errors 9.2.4 Oversampling A/D Converters 9.3 Digital-to-Analog Conversion 9.3.1 Sample and Hold 9.3.2 First-Order Hold 9.3.3 Linear Interpolation with Delay 9.3.4 Oversampling D/A Converters 9.4 Summary and References Problems 10 MULTIRATE DIGITAL SIGNAL PROCESSING 10.1 Introduction 10.2 Decimation by a Factor D 10.3 Interpolation by a Factor I 10.4 Sampling Rate Conversion by a Rational Factor I/D 10.5 Filter Design and Implementation for Sampling-Rate Conversion 10.5.1 Direct-Form FIR Filter Structures 10.5.2 Polyphase Filter Structures 10.5.3 Time-Variant Filter Structures 10.6 Multistage Implementation of Sampling-Rate Conversion 10.7 Sampling-Rate Conversion of Bandpass Signals 10.7.1 Decimation and Interpolation by Frequency Conversion 10.7.2 Modulation-Free Method for Decimation and Interpolation 10.8 Sampling-Rate Conversion by an Arbitrary Factor 10.8.1 First-Order Approximation 10.8.2 Second-Order Approximation Linear Interpolation 10.9 Applications of Multirate Signal Processing 10.9.1 Design of Phase Shifters 10.9.2 Interfacing of Digital Systems with Different Sampling Rates 10.9.3 Implementation of Narrowb and Lowpass Filters 10.9.4 Implementation of Digital Filter Banks 10.9.5 Subb and Coding of Speech Signals 10.9.6 Quadrature Mirror Filters 10.9.7 Transmultiplexers 10.9.8 0versampling A/D and D/A Conversion 10.10 Summary and References Problems 11 LINEAR PREDICTION AND OPTIMUM LINEAR FILTERS 11.1 Innovations Representation of a Stationary Random Process 11.1.1 Rational Power Spectra 11.1.2 Relationships Between the Filter Parameters and the Autocorrelation Sequence 11.2 Forward and Backward Linear Prediction 11.2.1 Forward Linear Prediction 11.2.2 Backward Linear Prediction 11.2.3 The Optimum Reflection Coefficients for the Lattice Forwardand Backward Predictors 11.2.4 Relationship of an AR Process to Linear Prediction 11.3 Solution of the Normal Equations 11.3.1 The Levinson-Durbin Algorithm 11.3.2 The Schiir Algorithm 11.4 Properties of the Linear Prediction-Error Filters 11.5 AR Lattice and ARMA Lattice-Ladder Filters 11.5.1 AR Lattice Structure 11.5.2 ARMA Processes and Lattice-Ladder Filters 11.6 Wiener Filters for Filtering and Prediction 11.6.1 FIR Wiener Filter 11.6.2 0rthogonality Principle in Linear Mean-Square Estimation 11.6.3 IIR Wiener Filter 11.6.4 Noncausal Wiener Filter 11.7 Summary and References Problems 12 POWER SPECTRUM ESTIMATION 12.1 Estimation of Spectra from Finite-Duration Observations of Signals 12.1.1 Computation of the Energy Density Spectrum 12.1.2 Estimation of the Autocorrelation and Power Spectrum of Random Signals:The Periodogram 12.1.3 The Use of the DFT in Power Spectrum Estimation 12.2 Nonparametric Methods for Power Spectrum Estimation 12.2.1 The Bartlett Method:Averaging Periodograms 12.2.2 The Welch Method:Averaging Modified Periodograms 12.2.3 The Blackmanand Tukey Method:Smoothing the Periodogram 12.2.4 Performance Characteristics of Nonparametric Power Spectrum Estimators 12.2.5 Computational Requirements of Nonparametric Power Spectrum Estimates 12.3 Parametric Methods for Power Spectrum Estimation 12.3.1 Relationships Between the Autocorrelation and the Model Parameters 12.3.2 The Yule-Walker Method for the AR Model Parameters 12.3.3 The Burg Method for the AR Model Parameters 12.3.4 Unconstrained Least-Squares Method for the AR Model Parameters 12.3.5 Sequential Estimation Methods for the AR Model Parameters 12.3.6 Selection of AR Model Order 12.3.7 MA Model for Power Spectrum Estimation 12.3.8 AR MA Model for Power Spectrum Estimation 12.3.9 Some Experimental Results 12.4 Minimum Variance Spectral Estimation 12.5 Eigenanalysis Algorithms for Spectrum Estimation 12.5.1 Pisarenko Harmonic Decomposition Method 12.5.2 Eigen-decomposition of the Autocorrelation Matrix for Sinusoids in White Noise 12.5.3 MUSIC Algorithm 12.5.4 ESPRIT Algorithm 12.5.5 Order Selection Criteria 12.5.6 Experimental Results 12.6 Summary and References Problems A RANDOM SIGNALS,CORRELATION FUNCTIONS,AND POWER SPECTRA B RANDOM NUMBER GENERATORS C TABLES OF TRANSITION COEFFICIENTS FOR THE DESIGN OF LINEAR-PHASEFIRFILTERS D LIST OF MATLAB FUNCTIONS REFERENCES AND BIBLIOGRAPHYR1 INDEX
数字信号处理原理、算法与应用（第三版·影印版）-图书简介:
为了给读者在理论和实践应用之间进行合理的平衡，本书严谨地介绍了现代数字信号处理的基本概念和技术，并介绍了相关的算法和应用。本书涵盖了线性离散时间系统分析的时域和频域方法，还涉及了诸如采样、数字滤波器设计、滤波器实现、去卷积、插值、状态矢量空间方法、频谱分析等相关主题的内容。本书不仅要求对诸多示例、练习的理解，而且更强调对数字信号算法进行软件实现的实践环节。 本书特点：·覆盖离散傅立叶变换（DFT）和快速傅立叶变换（FFT）算法，并对其进行了更加合理清晰的重组——介绍DFT，并在阐明傅立叶分析后描述其快速计算·描述模拟信号模数转换中涉及的运算和技术·在时域研究线性时不变离散时间系统和离散时间信号的特性·考虑双边z变换和单边z变换，并描述了求z反变换的方法·在频域分析信号与系统，给出连续时间信号与离散时间信号的傅立叶级数与傅立叶变换·实现无限冲激响应（IIR）与有限冲激响应（FIR）系统的结构形式，包括直接型、级联型、并联型、格型和格梯型·采样频率转换基础与多采样率转换系统·功率谱估计的详细测试，并讨论了非参数方法、基于模型的方法和基于特征分解的方法，包括MUSIC算法和ESPRIT算法·全书囊括了许多实例，并提供大约500个可解决的问题 本书既适合作为本科生学习离散系统和数字信号处理课程的教材，又适合研究生一年级学习数字信号处理课程时作为教材使用。
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