Research Catalog

Title

Algorithms for statistical signal processing / John G. Proakis [and others].

Publication

Upper Saddle River, N.J. : Prentice Hall, [2002], ©2002.

Items in the Library & Off-site

Holdings

Details

Additional Authors

Proakis, John G.

Description

xii, 564 pages : illustrations; 24 cm

Subject

• Signal processing > Mathematics
• Algorithms

Bibliography (note)

• Includes bibliographical references (p. 542-558) and index.

Contents

• 1. Introduction. 1.1. Characterization of Signals. 1.2. Characterization of Linear Time-Invariant Systems. 1.3. Sampling of Signals. 1.4. Linear Filtering Methods Based on the DFT. 1.5. The Cepstrum -- 2. Algorithms for Convolution and DFT. 2.1. Modulo Polynomials. 2.2. Circular Convolution as Polynomial Multiplication mod u[superscript N] - 1. 2.3. A Continued Fraction of Polynomials. 2.4. Chinese Remainder Theorem for Polynomials. 2.5. Algorithms for Short Circular Convolutions. 2.6. How We Count Multiplications. 2.7. Cyclotomic Polynomials. 2.8. Elementary Number Theory. 2.9. Convolution Length and Dimension. 2.10. The DFT as a Circular Convolution. 2.11. Winograd's DFT Algorithm. 2.12. Number-Theoretic Analogy of DFT. 2.13. Number-Theoretic Transform. 2.14. Split-Radix FFT. 2.15. Autogen Technique -- 3. Linear Prediction and Optimum Linear Filters. 3.1. Innovations Representation of a Stationary Random Process.
• 3.2. Forward and Backward Linear Prediction. 3.3. Solution of the Normal Equations. 3.4. Properties of the Linear Prediction-Error Filters. 3.5. AR Lattice and ARMA Lattice-Ladder Filters. 3.6. Wiener Filters for Filtering and Prediction -- 4. Least-Squares Methods for System Modeling and Filter Design. 4.1. System Modeling and Identification. 4.2. Least-Squares Filter Design for Prediction and Deconvolution. 4.3. Solution of Least-Squares Estimation Problems -- 5. Adaptive Filters. 5.1. Applications of Adaptive Filters. 5.2. Adaptive Direct-Form FIR Filters. 5.3. Adaptive Lattice-Ladder Filters -- 6. Recursive Least-Squares Algorithms for Array Signal Processing. 6.1. QR Decomposition for Least-Squares Estimation. 6.2. Gram-Schmidt Orthogonalization for Least-Squares Estimation. 6.3. Givens Algorithm for Time-Recursive Least-Squares Estimation. 6.4. Recursive Least-Squares Estimation Based on the Householder Transformation.
• 6.5. Order-Recursive Least-Squares Estimation Algorithms -- 7. QRD-Based Fast Adaptive Filter Algorithms. 7.1. Background. 7.2. QRD Lattice. 7.3. Multichannel Lattice. 7.4. Fast QR Algorithm. 7.5. Multichannel Fast QR Algorithm -- 8. Power Spectrum Estimation. 8.1. Estimation of Spectra from Finite-Duration Observations of Signals. 8.2. Nonparametric Methods for Power Spectrum Estimation. 8.3. Parametric Methods for Power Spectrum Estimation. 8.4. Minimum-Variance Spectral Estimation. 8.5. Eigenanalysis Algorithms for Spectrum Estimation -- 9. Signal Analysis with Higher-Order Spectra. 9.1. Use of Higher-Order Spectra in Signal Processing. 9.2. Definition and Properties of Higher-Order Spectra. 9.3. Conventional Estimators for Higher-Order Spectra. 9.4. Parametric Methods for Higher-Order Spectrum Estimation. 9.5. Cepstra of Higher-Order Spectra. 9.6. Phase and Magnitude Retrieval from the Bispectrum.

ISBN

0130622192

LCCN

2001036844

OCLC

• ocm47665287
• SCSB-4225604

Owning Institutions

Columbia University Libraries