Part III. Fast Fourier Transform Techniques
by C. Britton Rorabaugh
Notes on Digital Signal Processing: Practical Recipes for Design, Analysis and Implementation
- Title Page
- Copyright Page
- Dedication Page
- Contents
- Preface
- About the Author
- Part I. DSP Fundamentals
- Part II. Fourier Analysis
- Part III. Fast Fourier Transform Techniques
- Part IV. Window Techniques
- Part V. Classical Spectrum Analysis
- Part VI. FIR Filter Design
- Note 32. Designing FIR Filters: Background and Options
- Note 33. Linear-Phase FIR Filters
- Note 34. Periodicities in Linear-Phase FIR Responses
- Note 35. Designing FIR Filters: Basic Window Method
- Note 36. Designing FIR Filters: Kaiser Window Method
- Note 37. Designing FIR Filters: Parks-McClellan Algorithm
- Part V. Analog Prototype Filters
- Part VI. z-Transform Analysis
- Note 44. The z Transform
- Note 45. Computing the Inverse z Transform Using the Partial Fraction Expansion
- Note 46. Inverse z Transform via Partial Fraction Expansion: Case 1: All Poles Distinct with M < N in System Function
- Note 47. Inverse z Transform via Partial Fraction Expansion: Case 2: All Poles Distinct with M ≥ N in System Function (Explicit Approach)
- Note 48. Inverse z Transform via Partial Fraction Expansion: Case 3: All Poles Distinct with M ≥ N in System Function (Implicit Approach)
- Part VII. IIR Filter Design
- Part VIII. Multirate Signal Processing
- Part IX. Bandpass and Quadrature Techniques
- Note 58. Sampling Bandpass Signals
- Note 59. Bandpass Sampling: Wedge Diagrams
- Note 60. Complex and Analytic Signals
- Note 61. Generating Analytic Signals with FIR Hilbert Transformers
- Note 62. Generating Analytic Signals with Frequency-Shifted FIR Lowpass Filters
- Note 63. IIR Phase-Splitting Networks for Generating Analytic Signals
- Note 64. Generating Analytic Signals with Complex Equiripple FIR Filters
- Note 65. Generating I and Q Channels Digitally: Rader’s Approach
- Note 66. Generating I and Q Channels Digitally: Generalization of Rader’s Approach
- Part X. Statistical Signal Processing
- Note 67. Parametric Modeling of Discrete-Time Signals
- Note 68. Autoregressive Signal Models
- Note 69. Fitting AR Models to Stochastic Signals: The Yule-Walker Method
- Note 70. Fitting All-Pole Models to Deterministic Signals: Autocorrelation Method
- Note 71. Fitting All-Pole Models to Deterministic Signals: Covariance Method
- Note 72. Autoregressive Processes and Linear Prediction Analysis
- Note 73. Estimating Coefficients for Autoregressive Models: Burg Algorithm
- Index
- Footnotes
-
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