Başlık:
Algorithms for statistical signal processing
Yazar:
Proakis, John G.
ISBN:
9780130622198
Yayım Bilgisi:
Upper Saddle River, N.J. : Prentice Hall, c2002.
Fiziksel Tanım:
xii, 564 p. : ill. ; 24 cm.
Added Author:
Mevcut:*
Library | Materyal Türü | Barkod | Yer Numarası | Durum |
|---|---|---|---|---|
Searching... Pamukkale Merkez Kütüphanesi | Kitap | 0133177 | TK5102.9 A44 2002 | Searching... Unknown |
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Özet
Özet
Join Lau Lau, Nok Tok, Yojojo and De Li as they play and live together in Nara.
De Li and the Strawberries: When De Li discovers some strawberries growing in Nara, she shares them with her friends. But will there be enough for everyone?
Alıntılar
Alıntılar
The field of digital signal processing (DSP) has expanded rapidly over the past three decades. During the late sixties and seventies, we witnessed the development of the basic theory for digital filter design and the development of computationally efficient algorithms for evaluating the Fourier transform, convolution, and correlation. During the past two decades, we experienced an explosion in DSP applications spurred by significant advances in digital computer technology and integrated-circuit fabrication. In this period, the basic DSP theory has expanded to include parametric signal modeling, with applications to power spectrum estimation and system modeling, adaptive signal processing algorithms, multirate and multidimensional signal processing, and higher-order statistical methods for signal processing. With the expansion of basic DSP theory and the rapid growth in applications (spurred by the development of fast and inexpensive digital signal processors), there is a growing interest in advanced courses in DSP covering a variety of topics. This book was written with the goal of satisfying, in part, the resulting need for textbooks covering these advanced topics. Most of the material contained in this book was first published in 1992 by the Macmillan Publishing Company, in a book entitled Advanced Digital Signal Processing (which went out of print in 1997). This new book differs from the earlier publication by the inclusion of a new chapter (Chapter 7) on QRD-based fast adaptive filter algorithms, and the deletion of a chapter on multirate signal processing. The other chapters have remained essentially the same. The major focus of this book is on algorithms for statistical signal processing. Chapter 2 treats computationally efficient algorithms for convolution and for the computation of the discrete Fourier transform. Chapter 3 treats linear prediction and optimum Wiener filters; included in this chapter is a description of the Levinson-Durbin and Schur algorithms. Chapter 4 considers the filter design problem based on the least-squares method and describes several methods for solving least squares problems, including the Givens transformation, the Householder transformation, and singular-value decomposition. Chapter 5 treats single-channel adaptive filters based on the LMS algorithm and on recursive least-squares algorithms. Chapter 6 describes computationally efficient recursive least-squares algorithms for multichannel signals. Chapter 7 is focused on the uses of signal flow graphs for deriving computationally efficient adaptive filter algorithms based on the QR decomposition. Chapter 8 deals with power spectrum estimation, including both parametric and nonparametric methods. Chapter 9 describes the use of higher-order statistical methods for signal modeling and system identification. Although the material in this book was written by six different authors, we have tried very hard to maintain common notation throughout the book. We believe we have succeeded in developing a coherent treatment of the major topics outlined in the preceding overview. Chapter 1 provides an introduction to selected basic DSP material that is typically found in a first-level DSP text, and also serves to establish some of the notation used throughout the book. In our treatment of the various topics covered herein, we generally assume that the reader has had a prior course on the fundamentals of digital signal processing. The fundamental topics assumed as background include the z-transform, the analysis and characterization of discrete-time systems, the Fourier transform, the discrete Fourier transform (DFT), and the design of FIR and IIR digital filters. John G. Proakis Charles M. Rader Fuyun Ling Chrysostomos L. Nikias Marc Moonen Ian K Proudler Excerpted from Algorithms for Statistical Signal Processing by John G. Proakis, Charles M. Rader, Fuyun Ling, Marc Moonen, Ian K. Proudler, Chrysostomos L. Nikias All rights reserved by the original copyright owners. Excerpts are provided for display purposes only and may not be reproduced, reprinted or distributed without the written permission of the publisher.Table of Contents
| Preface | p. xi |
| 1 Introduction | p. 1 |
| 1.1 Characterization of Signals | p. 2 |
| 1.2 Characterization of Linear Time-Invariant Systems | p. 14 |
| 1.3 Sampling of Signals | p. 30 |
| 1.4 Linear Filtering Methods Based on the DFT | p. 46 |
| 1.5 The Cepstrum | p. 53 |
| 1.6 Summary and References | p. 56 |
| Problems | p. 56 |
| 2 Algorithms for Convolution and Dft | p. 61 |
| 2.1 Modulo Polynomials | p. 61 |
| 2.2 Circular Convolution as Polynomial Multiplication mod u[superscript N]--1 | p. 63 |
| 2.3 A Continued Fraction of Polynomials | p. 64 |
| 2.4 Chinese Remainder Theorem for Polynomials | p. 66 |
| 2.5 Algorithms for Short Circular Convolutions | p. 67 |
| 2.6 How We Count Multiplications | p. 74 |
| 2.7 Cyclotomic Polynomials | p. 76 |
| 2.8 Elementary Number Theory | p. 77 |
| 2.9 Convolution Length and Dimension | p. 88 |
| 2.10 The DFT as a Circular Convolution | p. 92 |
| 2.11 Winograd's DFT Algorithm | p. 95 |
| 2.12 Number-Theoretic Analogy of DFT | p. 98 |
| 2.13 Number-Theoretic Transform | p. 100 |
| 2.14 Split-Radix FFT | p. 110 |
| 2.15 Autogen Technique | p. 116 |
| 2.16 Summary | p. 122 |
| Problems | p. 123 |
| 3 Linear Prediction and Optimum Linear Filters | p. 125 |
| 3.1 Innovations Representation of a Stationary Random Process | p. 125 |
| 3.2 Forward and Backward Linear Prediction | p. 131 |
| 3.3 Solution of the Normal Equations | p. 140 |
| 3.4 Properties of the Linear Prediction-Error Filters | p. 148 |
| 3.5 AR Lattice and ARMA Lattice-Ladder Filters | p. 152 |
| 3.6 Wiener Filters for Filtering and Prediction | p. 157 |
| 3.7 Summary and References | p. 167 |
| Problems | p. 168 |
| 4 Least-Squares Methods for System Modeling and Filter Design | p. 177 |
| 4.1 System Modeling and Identification | p. 178 |
| 4.2 Least-Squares Filter Design for Prediction and Deconvolution | p. 189 |
| 4.3 Solution of Least-Squares Estimation Problems | p. 197 |
| 4.4 Summary and References | p. 225 |
| Problems | p. 226 |
| 5 Adaptive Filters | p. 231 |
| 5.1 Applications of Adaptive Filters | p. 231 |
| 5.2 Adaptive Direct-Form FIR Filters | p. 253 |
| 5.3 Adaptive Lattice-Ladder Filters | p. 276 |
| 5.4 Summary and References | p. 309 |
| Problems | p. 309 |
| 6 Recursive Least-Squares Algorithms for Array Signal Processing | p. 314 |
| 6.1 QR Decomposition for Least-Squares Estimation | p. 315 |
| 6.2 Gram-Schmidt Orthogonalization for Least-Squares Estimation | p. 318 |
| 6.3 Givens Algorithm for Time-Recursive Least-Squares Estimation | p. 337 |
| 6.4 Recursive Least-Squares Estimation Based on the Householder Transformation | p. 358 |
| 6.5 Order-Recursive Least-Squares Estimation Algorithms | p. 363 |
| 6.6 Summary and References | p. 382 |
| Problems | p. 384 |
| 7 QRD-Based Fast Adaptive Filter Algorithms | p. 387 |
| 7.1 Background | p. 388 |
| 7.2 QRD Lattice | p. 394 |
| 7.3 Multichannel Lattice | p. 402 |
| 7.4 Fast QR Algorithm | p. 411 |
| 7.5 Multichannel Fast QR Algorithm | p. 416 |
| 7.6 Summary and References | p. 427 |
| Problems | p. 429 |
| 8 Power Spectrum Estimation | p. 432 |
| 8.1 Estimation of Spectra from Finite-Duration Observations of Signals | p. 433 |
| 8.2 Nonparametric Methods for Power Spectrum Estimation | p. 445 |
| 8.3 Parametric Methods for Power Spectrum Estimation | p. 457 |
| 8.4 Minimum-Variance Spectral Estimation | p. 481 |
| 8.5 Eigenanalysis Algorithms for Spectrum Estimation | p. 483 |
| 8.6 Summary and References | p. 495 |
| Problems | p. 496 |
| 9 Signal Analysis with Higher-Order Spectra | p. 504 |
| 9.1 Use of Higher-Order Spectra in Signal Processing | p. 504 |
| 9.2 Definition and Properties of Higher-Order Spectra | p. 506 |
| 9.3 Conventional Estimators for Higher-Order Spectra | p. 514 |
| 9.4 Parametric Methods for Higher-Order Spectrum Estimation | p. 520 |
| 9.5 Cepstra of Higher-Order Spectra | p. 531 |
| 9.6 Phase and Magnitude Retrieval from the Bispectrum | p. 537 |
| 9.7 Summary and References | p. 540 |
| Problems | p. 541 |
| References | p. 542 |
| Index | p. 559 |
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