Linear Prediction Theory: A Mathematical Basis for Adaptive Systems

Linear Prediction Theory: A Mathematical Basis for Adaptive Systems

by Peter Strobach

Paperback(Softcover reprint of the original 1st ed. 1990)

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Product Details

ISBN-13: 9783642752087
Publisher: Springer Berlin Heidelberg
Publication date: 12/27/2011
Series: Springer Series in Information Sciences , #21
Edition description: Softcover reprint of the original 1st ed. 1990
Pages: 422
Product dimensions: 6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

1. Introduction.- 2. The Linear Prediction Model.- 2.1 The Normal Equations of Linear Prediction.- 2.2 Geometrical Interpretation of the Normal Equations.- 2.3 Statistical Interpretation of the Normal Equations.- 2.4 The Problem of Signal Observation.- 2.5 Recursion Laws of the Normal Equations.- 2.6 Stationarity — A Special Case of Linear Prediction.- 2.7 Covariance Method and Autocorrelation Method.- 2.8 Recursive Windowing Algorithms.- 2.9 Backward Linear Prediction.- 2.10 Chapter Summary.- 3. Classical Algorithms for Symmetric Linear Systems.- 3.1 The Cholesky Decomposition.- 3.2 The QR Decomposition.- 3.2.1 The Givens Reduction.- 3.2.2 The Householder Reduction.- 3.2.3 Calculation of Prediction Error Energy.- 3.3 Some More Principles for Matrix Computations.- 3.3.1 The Singular Value Decomposition.- 3.3.2 Solving the Normal Equations by Singular Value Decomposition.- 3.3.3 The Penrose Pseudoinverse.- 3.3.4 The Problem of Computing X?1Y.- 3.4 Chapter Summary.- 4. Recursive Least-Squares Using the QR Decomposition.- 4.1 Formulation of the Growing-Window Recursive Least-Squares Problem.- 4.2 Recursive Least Squares Based on the Givens Reduction.- 4.3 Systolic Array Implementation.- 4.4 Iterative Vector Rotations — The CORDIC Algorithm.- 4.5 Recursive QR Decomposition Using a Second-Order Window.- 4.6 Alternative Formulations of the QRLS Problem.- 4.7 Implicit Error Computation.- 4.8 Chapter Summary.- 5. Recursive Least-Squares Transversal Algorithms.- 5.1 The Recursive Least-Squares Algorithm.- 5.2 Potter’s Square-Root Normalized RLS Algorithm.- 5.3 Update Properties of the RLS Algorithm.- 5.4 Kubin’s Selective Memory RLS Algorithms.- 5.5 Fast RLS Transversal Algorithms.- 5.5.1 The Sherman-Morrison Identity for Partitioned Matrices.- 5.5.2 The Fast Kalman Algorithm.- 5.5.3 The FAEST Algorithm.- 5.6 Descent Transversal Algorithms.- 5.6.1 The Newton Algorithm.- 5.6.2 The Steepest Descent Algorithm.- 5.6.3 Stability of the Steepest Descent Algorithm.- 5.6.4 Convergence of the Steepest Descent Algorithm.- 5.6.5 The Least Mean Squares Algorithm.- 5.7 Chapter Summary.- 6. The Ladder Form.- 6.1 The Recursion Formula for Orthogonal Projections.- 6.1.1 Solving the Normal Equations with the Recursion Formula for Orthogonal Projections.- 6.1.2 The Feed-Forward Ladder Form.- 6.2 Computing Time-Varying Transversal Predictor Parameters from the Ladder Reflection Coefficients.- 6.3 Stationary Case — The PARCOR Ladder Form.- 6.4 Relationships Between PARCOR Ladder Form and Transversal Predictor.- 6.4.1 Computing Transversal Predictor Parameters from PARCOR Coefficients — The Levinson Recursion.- 6.4.2 Computing PARCOR Coefficients from Transversal Predictor Parameters — The Inverse Levinson Recursion.- 6.5 The Feed-Back PARCOR Ladder Form.- 6.6 Frequency Domain Description of PARCOR Ladder Forms.- 6.6.1 Transfer Function of the Feed-Forward PARCOR Ladder Form.- 6.6.2 Transfer Function of the Feed-Back PARCOR Ladder Form.- 6.6.3 Relationships Between Forward and Backward Predictor Transfer Functions.- 6.7 Stability of the Feed-Back PARCOR Ladder Form.- 6.8 Burg’s Harmonic Mean PARCOR Ladder Algorithm.- 6.9 Determination of Model Order.- 6.10 Chapter Summary.- 7. Levinson-Type Ladder Algorithms.- 7.1 The Levinson-Durbin Algorithm.- 7.2 Computing the Autocorrelation Coefficients from the PARCOR Ladder Reflection Coefficients — The “Inverse” Levinson-Durbin Algorithm.- 7.3 Some More Properties of Toeplitz Systems and the Levinson-Durbin Algorithm.- 7.4 Split Levinson Algorithms.- 7.4.1 Delsarte’s Algorithm.- 7.4.2 Krishna’s Algorithm.- 7.4.3 Relationships Between Krishna’s Algorithm and Delsarte’s Algorithm (Symmetric Case).- 7.4.4 Relationships Between Krishna’s Algorithm and Delsarte’s Algorithm (Antisymmetric Case).- 7.5 A Levinson-Type Least-Squares Ladder Estimation Algorithm.- 7.6 The Makhoul Covariance Ladder Algorithm.- 7.7 Chapter Summary.- 8 Covariance Ladder Algorithms.- 8.1 The LeRoux-Gueguen Algorithm.- 8.1.1 Bounds on GREs.- 8.2 The Cumani Covariance Ladder Algorithm.- 8.3 Recursive Covariance Ladder Algorithms.- 8.3.1 Recursive Least-Squares Using Generalized Residual Energies.- 8.3.2 Strobach’s Algorithm.- 8.3.3 Approximate PORLA Computation Schemes.- 8.3.4 Sokat’s Algorithm — An Extension of the LeRoux-Gueguen Algorithm.- 8.3.5 Additional Notes on Sokat’s PORLA Method.- 8.4 Split Schur Algorithms.- 8.4.1 A Split Schur Formulation of Krishna’s Algorithm.- 8.4.2 Bounds on Recursion Variables.- 8.4.3 A Split Schur Formulation of Delsarte’s Algorithm.- 8.5 Chapter Summary.- 9. Fast Recursive Least-Squares Ladder Algorithms.- 9.1 The Exact Time-Update Theorem of Projection Operators.- 9.2 The Algorithm of Lee and Morf.- 9.3 Other Forms of Lee’s Algorithm.- 9.3.1 A Pure Time Recursive Ladder Algorithm.- 9.3.2 Direct Updating of Reflection Coefficients.- 9.4 Gradient Adaptive Ladder Algorithms.- 9.4.1 Gradient Adaptive Ladder Algorithm GAL 2.- 9.4.2 Gradient Adaptive Ladder Algorithm GAL 1.- 9.5 Lee’s Normalized RLS Ladder Algorithm.- 9.5.1 Power Normalization.- 9.5.2 Angle Normalization.- 9.6 Chapter Summary.- 10. Special Signal Models and Extensions.- 10.1 Joint Process Estimation.- 10.1.1 Ladder Formulation of the Joint Process Problem.- 10.1.2 The Joint Process Model.- 10.1.3 FIR System Identification.- 10.1.4 Noise Cancelling.- 10.2 ARMA System Identification.- 10.2.1 The ARMA Normal Equations.- 10.2.2 ARMA Embedding.- 10.2.3 Ladder Formulation of the ARMA System Identification Problem.- 10.2.4 The PORLA Method for ARMA System Identification.- 10.2.5 Computing the ARMA Parameters from the Ladder Reflection Matrices.- 10.3 Identification of Vector Autoregressive Processes.- 10.4 Parametric Spectral Estimation.- 10.5 Relationships Between Parameter Estimation and Kalman Filter Theory.- 10.6 Chapter Summary.- 11. Concluding Remarks and Applications.- A.1 Summary of the Most Important Forward/Backward Linear Prediction Relationships.- A.2 New PORLA Algorithms and Their Systolic Array Implementation.- A.2.1 Triangular Array Ladder Algorithm ARRAYLAD 1.- A.2.2 Triangular Array Ladder Algorithm ARRAYLAD 2.- A.2.3 Systolic Array Implementation.- A.2.4 Comparison of Ladder and Givens Rotors.- A.2.5 A Square-Root PORLA Algorithm.- A.2.6 A Step Towards Toeplitz Systems.- A.3 Vector Case of New PORLA Algorithms.- A.3.1 Vector Case of ARRAYLAD 1.- A.3.2 Computation of Reflection Matrices.- A.3.3 Multichannel Prediction Error Filters.- A.3.4 Vector Case of ARRAYLAD 2.- A.3.5 Stationary Case — Block Processing Algorithms.- A.3.6 Concluding Remarks.

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