Digital filter design and realization /

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Analysis, design, and realization of digital filters have experienced major developments since the 1970s, and have now become an integral part of the theory and practice in the field of contemporary digital signal processing. Digital Filter Design and Realization is written to present an up-to-date...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Hinamoto, Takao (Autor), Lu, Wusheng (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Aalborg : River Publishers, 2017.
Colección:River Publishers series in signal, image and speech processing.
Temas:
Electric filters, Digital.
Signal processing > Digital techniques.
Filtres numériques.
Traitement du signal > Techniques numériques.
SCIENCE / Energy
TECHNOLOGY / Electricity
TECHNOLOGY / Electronics / General
Electric filters, Digital
Signal processing > Digital techniques
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Half Title Page
  • RIVER PUBLISHERS SERIES IN SIGNAL, IMAGE AND SPEECH PROCESSING
  • Title Page
  • Digital Filter Design and Realization
  • Copyright Page
  • Contents
  • Preface
  • List of Figures
  • List of Tables
  • List of Abbreviations
  • Chapter 1
  • Introduction
  • 1.1 Preview
  • 1.2 Terminology for Signal Analysis and Typical Signals
  • 1.2.1 Terminology for Signal Analysis
  • 1.2.2 Examples of Typical Signals
  • 1.3 Digital Signal Processing
  • 1.3.1 General Framework for Digital Signal Processing
  • 1.3.2 Advantages of Digital Signal Processing
  • 1.3.3 Disadvantages of Digital Signal Processing
  • 1.4 Analysis of Analog Signals
  • 1.4.1 The Fourier Series Expansion of Periodic Signals
  • 1.4.2 The Fourier Transform
  • 1.4.3 The Laplace Transform
  • 1.5 Analysis of Discrete-Time Signals
  • 1.5.1 Sampling an Analog Signal
  • 1.5.2 The Discrete-Time Fourier Transform
  • 1.5.3 The Discrete Fourier Transform (DFT)
  • 1.5.4 The z-Transform
  • 1.6 Sampling of Continuous-Time Sinusoidal Signals
  • 1.7 Aliasing
  • 1.8 Sampling Theorem
  • 1.9 Recovery of an Analog Signal
  • 1.10 Summary
  • References
  • Chapter 2
  • Discrete-Time Systems and z-Transformation
  • 2.1 Preview
  • 2.2 Discrete-Time Signals
  • 2.3 z-Transform of Basic Sequences
  • 2.3.1 Fundamental Transforms
  • 2.3.2 Properties of z-Transform
  • 2.4 Inversion of z-Transforms
  • 2.4.1 Partial Fraction Expansion
  • 2.4.2 Power Series Expansion
  • 2.4.3 Contour Integration
  • 2.5 Parseval's Theorem
  • 2.6 Discrete-Time Systems
  • 2.7 Difference Equations
  • 2.8 State-Space Descriptions
  • 2.8.1 Realization 1
  • 2.8.2 Realization 2
  • 2.9 Frequency Transfer Functions
  • 2.9.1 Linear Time-Invariant Causal Systems
  • 2.9.2 Rational Transfer Functions
  • 2.9.3 All-Pass Digital Filters
  • 2.9.4 Notch Digital Filters
  • 2.9.5 Doubly Complementary Digital Filters
  • 2.10 Summary.
  • 5.9.2 Analytical Approach
  • 5.9.2.1 General FIR filter design
  • 5.9.2.2 Linear-Phase FIR filter design
  • 5.9.3 Chebyshev Approximation
  • 5.9.4 Comparison of Algorithms' Performances
  • 5.10 Summary
  • References
  • Chapter 6
  • Design Methods Using Analog Filter Theory
  • 6.1 Preview
  • 6.2 Design Methods Using Analog Filter Theory
  • 6.2.1 Lowpass Analog-Filter Approximations
  • 6.2.1.1 Butterworth approximation
  • 6.2.1.2 Chebyshev approximation
  • 6.2.1.3 Inverse-Chebyshev approximation
  • 6.2.1.4 Elliptic approximation
  • 6.2.2 Other Analog-Filter Approximations by Transformations
  • 6.2.2.1 Lowpass-to-lowpass transformation
  • 6.2.2.2 Lowpass-to-highpass transformation
  • 6.2.2.3 Lowpass-to-bandpass transformation
  • 6.2.2.4 Lowpass-to-bandstop transformation
  • 6.2.3 Design Methods Based on Analog Filter Theory
  • 6.2.3.1 Invariant impulse-response method
  • 6.2.3.2 Bilinear-transformation method
  • 6.3 Summary
  • References
  • Chapter 7
  • Design Methods in the Frequency Domain
  • 7.1 Preview
  • 7.2 Design Methods in the Frequency Domain
  • 7.2.1 Minimum Mean Squared Error Design
  • 7.2.2 An Equiripple Design by Linear Programming
  • 7.2.3 Weighted Least-Squares Design with Stability Constraints
  • 7.2.4 Minimax Design with Stability Constraints
  • 7.3 Design of All-Pass Digital Filters
  • 7.3.1 Design of All-Pass Filters Based on Frequency Response Error
  • 7.3.2 Design of All-Pass Filters Based on Phase Characteristic Error
  • 7.3.3 A Numerical Example
  • 7.4 Summary
  • References
  • Chapter 8
  • Design Methods in the Time Domain
  • 8.1 Preview
  • 8.2 Design Based on Extended Pade's Approximation
  • 8.2.1 A Direct Procedure
  • 8.2.2 A Modified Procedure
  • 8.3 Design Using Second-Order Information
  • 8.3.1 A Filter Design Method
  • 8.3.2 Stability
  • 8.3.3 An Efficient Algorithm for Solving (8.35)
  • 8.4 Least-Squares Design.
  • 8.5 Design Using State-Space Models
  • 8.5.1 Balanced Model Reduction
  • 8.5.2 Stability and Minimality
  • 8.6 Numerical Experiments
  • 8.6.1 Design Based on Extended Pade's Approximation
  • 8.6.2 Design Using Second-Order Information
  • 8.6.3 Least-Squares Design
  • 8.6.4 Design Using State-Space Model (Balanced Model Reduction)
  • 8.6.5 Comparison of Algorithms' Performances
  • 8.7 Summary
  • References
  • Chapter 9
  • Design of Interpolated and FRM FIR Digital Filters
  • 9.1 Preview
  • 9.2 Basics of IFIR and FRM Filters and CCP
  • 9.2.1 Interpolated FIR Filters
  • 9.2.2 Frequency-Response-Masking Filters
  • 9.2.3 Convex-Concave Procedure (CCP)
  • 9.3 Minimax Design of IFIR Filters
  • 9.3.1 Problem Formulation
  • 9.3.2 Convexification of (9.10) Using CCP
  • 9.3.3 Remarks on Convexification in (9.13)-(9.14)
  • 9.4 Minimax Design of FRM Filters
  • 9.4.1 The Design Problem
  • 9.4.2 A CCP Approach to Solving (9.23)
  • 9.5 FRM Filters with Reduced Complexity
  • 9.5.1 Design Phase 1
  • 9.5.2 Design Phase 2
  • 9.6 Design Examples
  • 9.6.1 Design and Evaluation Settings
  • 9.6.2 Design of IFIR Filters
  • 9.6.3 Design of FRM Filters
  • 9.6.4 Comparisons with Conventional FIR Filters
  • 9.7 Summary
  • References
  • Chapter 10
  • Design of a Class of Composite Digital Filters
  • 10.1 Preview
  • 10.2 Composite Filters and Problem Formulation
  • 10.2.1 Composite Filters
  • 10.2.2 Problem Formulation
  • 10.3 Design Method
  • 10.3.1 Design Strategy
  • 10.3.2 Solving (10.7) with y Fixed to y = yk
  • 10.3.3 Updating y with x Fixed to x = xk
  • 10.3.4 Summary of the Algorithm
  • 10.4 Design Example and Comparisons
  • 10.5 Summary
  • References
  • Chapter 11
  • FiniteWord Length Effects
  • 11.1 Preview
  • 11.2 Fixed-Point Arithmetic
  • 11.3 Floating-Point Arithmetic
  • 11.4 Limit Cycles-Overflow Oscillations.
  • 11.5 Scaling Fixed-Point Digital Filters to Prevent Overflow
  • 11.6 Roundoff Noise
  • 11.7 Coefficient Sensitivity
  • 11.8 State-Space Descriptions with FiniteWord Length
  • 11.9 Limit Cycle-Free Realization
  • 11.10 Summary
  • References
  • Chapter 12
  • l2-Sensitivity Analysis and Minimization
  • 12.1 Preview
  • 12.2 l2-Sensitivity Analysis
  • 12.3 Realization with Minimal l2-Sensitivity
  • 12.4 l2-Sensitivity Minimization Subject to l2-Scaling Constraints Using Quasi-Newton Algorithm
  • 12.4.1 l2-Scaling and Problem Formulation
  • 12.4.2 Minimization of (12.18) Subject to l2-Scaling Constraints
  • Using Quasi-Newton Algorithm
  • 12.4.3 Gradient of J(x)
  • 12.5 l2-Sensitivity Minimization Subject to l2-Scaling Constraints Using Lagrange Function
  • 12.5.1 Minimization of (12.19) Subject to l2-Scaling Constraints
  • Using Lagrange Function
  • 12.5.2 Derivation of Nonsingular T from P to Satisfy l2-Scaling Constraints
  • 12.6 Numerical Experiments
  • 12.6.1 Filter Description and Initial l2-Sensitivity
  • 12.6.2 l2-Sensitivity Minimization
  • 12.6.3 l2-Sensitivity Minimization Subject to l2-Scaling Constraints Using Quasi-Newton Algorithm
  • 12.6.4 l2-Sensitivity Minimization Subject to l2-Scaling Constraints Using Lagrange Function
  • 12.7 Summary
  • References
  • Chapter 13
  • Pole and Zero Sensitivity Analysis and Minimization
  • 13.1 Preview
  • 13.2 Pole and Zero Sensitivity Analysis
  • 13.3 Realization with Minimal Pole and Zero Sensitivity
  • 13.3.1 Weighted Pole and Zero Sensitivity Minimization WithoutImposing l2-Scaling Constraints
  • 13.3.2 Zero Sensitivity Minimization Subject to Minimal Pole Sensitivity
  • 13.4 Pole Zero Sensitivity Minimization Subject to l2-Scaling Constraints Using Lagrange Function
  • 13.4.1 l2-Scaling Constraints and Problem Formulation.
  • 13.4.2 Minimization of (13.37) Subject to l2-Scaling Constraints
  • Using Lagrange Function.

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