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Mathmatical method and algorithms for signal processing

Mathematical Methods and Algorithms
for
Signal Processing
Todd K. Moon
Utah State University

Wynn С Stirling
Brigham Young University

PRENTICE HALL
Upper Saddle River, NJ 07458

This previously included a CD. The
CD contents can now be accessed
at www.prenhall.com/moon. Thank You.


Contents
1
1


II
2

Introduction and Foundations

1

Introduction and Foundations
1.1
What is signal processing?
1.2
Mathematical topics embraced by signal processing
1.3
Mathematical models
1.4
Models for linear systems and signals
1.4.1 Linear discrete-time models
1.4.2 Stochastic MA and AR models
1.4.3 Continuous-time notation
1.4.4 Issues and applications
1.4.5 Identification of the modes
1.4.6 Control of the modes
1.5
Adaptive
filtering
1.5.1 System identification
1.5.2 Inverse system identification
1.5.3 Adaptive predictors
1.5.4 Interference cancellation
1.6
Gaussian random variables and random processes
1.6.1 Conditional Gaussian densities
1.7
Markov and Hidden Markov Models
1.7.1 Markov models
1.7.2 Hidden Markov models
1.8
Some aspects of proofs
1.8.1 Proof by computation: direct proof
1.8.2 Proof by contradiction


1.8.3 Proof by induction
1.9
An application: LFSRs and Massey's algorithm
1.9.1 Issues and applications of LFSRs
1.9.2 Massey's algorithm
1.9.3 Characterization of LFSR length in Massey's algorithm
1.10 Exercises
1.11 References

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Vector Spaces and Linear Algebra

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Signal Spaces
2.1
Metric spaces
2.1.1 Some topological terms
2.1.2 Sequences, Cauchy sequences, and completeness

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Contents

2.2

2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
2.14
2.15
2.16
2.17
2.18

2.1.3 Technicalities associated with the Lp and L^ spaces
Vector spaces
2.2.1
Linear combinations of vectors
2.2.2
Linear independence
2.2.3
Basis and dimension
2.2.4
Finite-dimensional vector spaces and matrix notation
Norms and normed vector spaces
2.3.1
Finite-dimensional normed linear spaces
Inner products and inner-product spaces
2.4.1
Weak convergence
Induced norms
The Cauchy-Schwarz inequality
Direction of vectors: Orthogonality
Weighted inner products
2.8.1
Expectation as an inner product
Hilbert and Banach spaces
Orthogonal subspaces
Linear transformations: Range and nullspace
Inner-sum and direct-sum spaces
Projections and orthogonal projections
2.13.1 Projection matrices
The projection theorem
Orthogonalization of vectors
Some final technicalities for infinite dimensional spaces
Exercises
References

Representation and Approximation in Vector Spaces
3.1
The approximation problem in Hilbert space
3.1.1
The Grammian matrix
3.2
The orthogonality principle
3.2.1
Representations in infinite-dimensional space
3.3
Error minimization via gradients
3.4
Matrix representations of least-squares problems
3.4.1
Weighted least-squares
3.4.2
Statistical properties of the least-squares estimate
3.5
Minimum error in Hilbert-space approximations

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Applications of the orthogonality theorem
3.6
3.7
3.8
3.9

3.10
3.11
3.12
3.13

Approximation by continuous polynomials
Approximation by discrete polynomials
Linear regression
Least-squares
filtering
3.9.1
Least-squares prediction and AR spectrum
estimation
Minimum mean-square estimation
Minimum mean-squared error (MMSE)
filtering
Comparison of least squares and minimum mean squares
Frequency-domain optimal
filtering
3.13.1 Brief review of stochastic processes and
Laplace transforms

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Contents

3.13.2

3.14
3.15
3.16
3.17
3.18

3.19

3.20
3.21
4

Two-sided Laplace transforms and their
decompositions
3.13.3 The Wiener-Hopf equation
3.13.4 Solution to the Wiener-Hopf equation
3.13.5 Examples of Wiener
filtering
3.13.6 Mean-square error
3.13.7 Discrete-time Wiener filters
A dual approximation problem
Minimum-norm solution of underdetermined equations
Iterative Reweighted LS (IRLS) for Lp optimization
Signal transformation and generalized Fourier series
Sets of complete orthogonal functions
3.18.1 Trigonometric functions
3.18.2 Orthogonal polynomials
3.18.3 Sine functions
3.18.4 Orthogonal wavelets
Signals as points: Digital communications
3.19.1 The detection problem
3.19.2 Examples of basis functions used in digital
communications
3.19.3 Detection in nonwhite noise
Exercises
References

Linear Operators and Matrix Inverses
4.1
Linear operators
4.1.1
Linear functionals
4.2
Operator norms
4.2.1
Bounded operators
4.2.2
The Neumann expansion
4.2.3
Matrix norms
4.3
Adjoint operators and transposes
4.3.1
A dual optimization problem
4.4
Geometry of linear equations
4.5
Four fundamental subspaces of a linear operator
4.5.1
The four fundamental subspaces with
non-closed range
4.6
Some properties of matrix inverses
4.6.1
Tests for invertibility of matrices
4.7
Some results on matrix rank
4.7.1
Numeric rank
4.8
Another look at least squares
4.9
Pseudoinverses
4.10 Matrix condition number
4.11 Inverse of a small-rank adjustment
4.11.1 An application: the RLS
4.11.2 Two RLS applications
4.12 Inverse of a block (partitioned) matrix
4.12.1 Application: Linear models
4.13 Exercises
4.14 References

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filter

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Contents

viii

5

Some Important Matrix Factorizations
5.1
The LU factorization
5.1.1
Computing the determinant using the LU factorization
5.1.2
Computing the LU factorization
5.2
The Cholesky factorization
5.2.1
Algorithms for computing the Cholesky factorization
5.3
Unitary matrices and the QR factorization
5.3.1
Unitary matrices
5.3.2
The QR factorization
5.3.3
QR factorization and least-squares
filters
5.3.4
Computing the QR factorization
5.3.5
Householder transformations
5.3.6
Algorithms for Householder transformations
5.3.7
QR factorization using Givens rotations
5.3.8
Algorithms for QR factorization using Givens rotations
5.3.9
Solving least-squares problems using Givens rotations
5.3.10 Givens rotations via CORDIC rotations
5.3.11 Recursive updates to the QR factorization
5.4
Exercises
5.5
References

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6

Eigenvalues and Eigenvectors
6.1
Eigenvalues and linear systems
6.2
Linear dependence of eigenvectors
6.3
Diagonalization of a matrix
6.3.1
The Jordan form
6.3.2
Diagonalization of self-adjoint matrices
6.4
Geometry of invariant subspaces
6.5
Geometry of quadratic forms and the minimax principle
6.6
Extremal quadratic forms subject to linear constraints
6.7
The Gershgorin circle theorem

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324

Application of Eigendecomposition methods
6.8
6.9

6.10

6.11
6.12

6.13
6.14

Karhunen-Loeve low-rank approximations and principal methods —
6.8.1
Principal component methods
Eigenfilters
6.9.1
Eigenfilters for random signals
6.9.2
Eigenfilter for designed spectral response
6.9.3
Constrained eigenfilters
Signal subspace techniques
6.10.1 The signal model
6.10.2 The noise model
6.10.3 Pisarenko harmonic decomposition
6.10.4 MUSIC
Generalized eigenvalues
6.11.1 An application: ESPRIT
Characteristic and minimal polynomials
6.12.1 Matrix polynomials
6.12.2 Minimal polynomials
Moving the eigenvalues around: Introduction to linear control
Noiseless constrained channel capacity

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6.15

6.16
6.17

Computation of eigenvalues and eigenvectors
6.15.1 Computing the largest and smallest eigenvalues
6.15.2 Computing the eigenvalues of a symmetric matrix
6.15.3 The QR iteration
Exercises
References

The Singular Value Decomposition
7.1
Theory of the SVD
7.2
Matrix structure from the SVD
7.3
Pseudoinverses and the SVD
7.4
Numerically sensitive problems
7.5
Rank-reducing approximations: Effective rank
Applications of the SVD
7.6
System identification using the SVD
7.7
Total least-squares problems
7.7.1 Geometric interpretation of the TLS solution
7.8
Partial total least squares
7.9
Rotation of subspaces
7.10 Computation of the SVD
7.11 Exercises
7.12 References

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Some Special Matrices and Their Applications
8.1
Modal matrices and parameter estimation
8.2
Permutation matrices
8.3
Toeplitz matrices and some applications
8.3.1
Durbin's algorithm
8.3.2
Predictors and lattice filters
8.3.3
Optimal predictors and Toeplitz inverses
8.3.4
Toeplitz equations with a general right-hand side
8.4
Vandermonde matrices
8.5
Circulant matrices
8.5.1
Relations among Vandermonde, circulant, and
companion matrices
8.5.2
Asymptotic equivalence of the eigenvalues of Toeplitz and
circulant matrices
8.6
Triangular matrices
8.7
Properties preserved in matrix products
8.8
Exercises
8.9
References

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407
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410

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Kronecker Products and the Vec Operator
9.1
The Kronecker product and Kronecker sum
9.2
Some applications of Kronecker products
9.2.1
Fast Hadamard transforms
9.2.2
DFT computation using Kronecker products
9.3 The vec operator
9.4 Exercises
9.5 References

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X

III

Detection, Estimation, and Optimal Filtering

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10

Introduction to Detection and Estimation, and Mathematical Notation
10.1 Detection and estimation theory
10.1.1 Game theory and decision theory
10.1.2 Randomization
10.1.3 Special cases
10.2 Some notational conventions
10.2.1 Populations and statistics
10.3 Conditional expectation
10.4 Transformations of random variables
10.5 Sufficient statistics
10.5.1 Examples of sufficient statistics
10.5.2 Complete sufficient statistics
10.6 Exponential families
10.7 Exercises
10.8 References

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11

Detection Theory
11.1 Introduction to hypothesis testing
11.2 Neyman-Pearson theory
11.2.1 Simple binary hypothesis testing
11.2.2 The Neyman-Pearson lemma
11.2.3 Application of the Neyman-Pearson lemma
11.2.4 The likelihood ratio and the receiver operating
characteristic (ROC)
11.2.5 A Poisson example
11.2.6 Some Gaussian examples
11.2.7 Properties of the ROC
11.3 Neyman-Pearson testing with composite binary hypotheses
11.4 Bayes decision theory
11.4.1 The Bayes principle
11.4.2 The risk function
11.4.3 Bayes
risk
11.4.4 Bayes tests of simple binary hypotheses
11.4.5 Posterior distributions
11.4.6 Detection and sufficiency
11.4.7 Summary of binary decision problems
11.5 Some M-ary problems
11.6 Maximum-likelihood detection
11.7 Approximations to detection performance: The union bound
11.8 Invariant Tests
11.8.1 Detection with random (nuisance) parameters
11.9
Detection in continuous time
11.9.1
Some extensions and precautions
11.10 Minimax Bayes decisions
11.10.1 Bayes envelope function
11.10.2 Minimax rules
11.10.3 Minimax Bayes in multiple-decision problems

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11.11
11.12

11.10.4 Determining the least favorable prior
11.10.5 A minimax example and the minimax theorem
Exercises
References

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541

Estimation Theory
12.1
The maximum-likelihood principle
12.2
ML estimates and sufficiency
12.3
Estimation quality
12.3.1
The score function
12.3.2
The Cramer-Rao lower bound
12.3.3
Efficiency
12.3.4
Asymptotic properties of maximum-likelihood
estimators
12.3.5
The multivariate normal case
12.3.6
Minimum-variance unbiased estimators
12.3.7
The linear statistical model
12.4
Applications of ML estimation
12.4.1
ARMA parameter estimation
12.4.2
Signal subspace identification
12.4.3
Phase estimation
12.5
Bayes estimation theory
12.6
Bayes risk
12.6.1
MAP estimates
p 12.6.2
Summary
12.6.3
Conjugate prior distributions
12.6.4
Connections with minimum mean-squared
estimation
12.6.5
Bayes estimation with the Gaussian distribution
12.7
Recursive estimation
12.7.1
An example of non-Gaussian recursive Bayes
12.8
Exercises
12.9
References

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552

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The Kaiman Filter
13.1
The state-space signal model
13.2
Kaiman filter I: The Bayes approach
13.3
Kaiman filter II: The innovations approach
13.3.1
Innovations for processes with linear observation models.
13.3.2
Estimation using the innovations process
,
13.3.3
Innovations for processes with state-space models
13.3.4
A recursion for P„ r _|
13.3.5
The discrete-time Kaiman
filter
13.3.6
Perspective
13.3.7
Comparison with the RLS adaptive filter algorithm
13.4 Numerical considerations: Square-root
filters
13.5 Application in continuous-time systems
13.5.1 Conversion from continuous time to discrete time
13.5.2 A simple kinematic example
13.6 Extensions of Kaiman filtering to nonlinear systems

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Contents

Smoothing
13.7.1 The Rauch-Tung-Streibel fixed-interval smoother
13.8 Another approach: Я«, smoothing
13.9 Exercises
13.10 References

IV
14

15

13.7

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620

Iterative and Recursive Methods in Signal Processing

621

Basic Concepts and Methods of Iterative Algorithms
14.1 Definitions and qualitative properties of iterated
functions
14.1.1 Basic theorems of iterated functions
14.1.2 Illustration of the basic theorems
14.2 Contraction mappings
14.3 Rates of convergence for iterative algorithms
14.4 Newton's method
14.5 Steepest descent
14.5.1 Comparison and discussion: Other techniques
Some Applications of Basic Iterative Methods
14.6 LMS adaptive Filtering
14.6.1 An example LMS application
14.6.2 Convergence of the LMS algorithm
14.7 Neural networks
14.7.1 The backpropagation training algorithm
14.7.2 The nonlinearity function
14.7.3 The forward-backward training algorithm
14.7.4 Adding a momentum term
14.7.5 Neural network code
14.7.6 How many neurons?
14.7.7 Pattern recognition: ML or NN?
14.8 Blind source separation
14.8.1 A bit of information theory
14.8.2 Applications to source separation
14.8.3 Implementation aspects
14.9 Exercises
14.10 References
Iteration by Composition of Mappings
15.1 Introduction
15.2 Alternating projections
15.2.1 An applications: bandlimited reconstruction
15.3 Composite mappings
15.4 Closed mappings and the global convergence theorem
15.5 The composite mapping algorithm
15.5.1 Bandlimited reconstruction, revisited
15.5.2 An example: Positive sequence determination
15.5.3 Matrix property mappings
15.6 Projection on convex sets
15.7 Exercises
15.8 References

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Contents

xiii

16

Other Iterative Algorithms
16.1 Clustering
16.1.1 An example application: Vector quantization
16.1.2 An example application: Pattern recognition
16.1.3 к -means Clustering
16.1.4 Clustering using fuzzy к -means
16.2 Iterative methods for computing inverses of matrices
16.2.1 The Jacobi method
16.2.2 Gauss-Seidel iteration
16.2.3 Successive over-relaxation (SOR)
16.3 Algebraic reconstruction techniques (ART)
16.4 Conjugate-direction methods
16.5 Conjugate-gradient method
16.6 Nonquadratic problems
16.7 Exercises
16.8 References

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17

The EM Algorithm in Signal Processing
17.1 An introductory example
17.2 General statement of the EM algorithm
17.3 Convergence of the EM algorithm
17.3.1 Convergence rate: Some generalizations
Example applications of the EM algorithm
17.4 Introductory example, revisited
17.5 Emission computed tomography (ЕСТ) image reconstruction
17.6 Active noise cancellation (ANC)
17.7 Hidden Markov models
17.7.1 The E-and M-steps
r,
r
17.7.2 The forward and backward probabilities
17.7.3 Discrete output densities
17.7.4 Gaussian output densities
17.7.5 Normalization
17.7.6 Algorithms for HMMs
17.8 Spread-spectrum, multiuser communication
17.9 Summary
17.10 Exercises
17.11 References

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V

Methods of Optimization

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18

Theory of Constrained Optimization
18.1 Basic definitions
18.2 Generalization of the chain rule to composite functions
18.3 Definitions for constrained optimization
18.4 Equality constraints: Lagrange multipliers
18.4.1 Examples of equality-constrained optimization
18.5 Second-order conditions
18.6 Interpretation of the Lagrange multipliers
18.7 Complex constraints
......
18.8 Duality in optimization

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Contents

xiv

19

18.9

Inequality constraints: Kuhn-Tucker conditions
18.9.1 Second-order conditions for inequality constraints
18.9.2 An extension: Fritz John conditions
18.10 Exercises
18.11 References

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786

Shortest-Path Algorithms and Dynamic Programming
19.1 Definitions for graphs
19.2 Dynamic programming
19.3 The Viterbi algorithm
19.4 Code for the Viterbi algorithm
19.4.1 Related algorithms: Dijkstra's and Warshall's
19.4.2 Complexity comparisons of Viterbi and Dijkstra

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Applications of path search algorithms
19.5

Maximum-likelihood sequence estimation
19.5.1 The intersymbol interference (ISI) channel
19.5.2 Code-division multiple access
19.5.3 Convolutional decoding
HMM likelihood analysis and HMM training
19.6.1 Dynamic warping
Alternatives to shortest-path algorithms
Exercises
References

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817

Linear Programming
20.1 Introduction to linear programming
20.2 Putting a problem into standard form
20.2.1 Inequality constraints and slack variables
20.2.2 Free variables
20.2.3 Variable-bound constraints
20.2.4 Absolute value in the objective
20.3 Simple examples of linear programming
20.4 Computation of the linear programming solution
20.4.1 Basic variables
20.4.2 Pivoting
20.4.3 Selecting variables on which to pivot
20.4.4 The effect of pivoting on the value of the problem
20.4.5 Summary of the simplex algorithm
20.4.6 Finding the initial basic feasible solution
20.4.7 MATLAB® code for linear programming
20.4.8 Matrix notation for the simplex algorithm
20.5 Dual problems
20.6 Karmarker's algorithm for LP
20.6.1 Conversion to Karmarker standard form
20.6.2 Convergence of the algorithm
20.6.3 Summary and extensions

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19.6
19.7
19.8
19.9
20

Examples and applications of linear programming
20.7
20.8

Linear-phase FIR filter design
20.7.1 Least-absolute-error approximation
Linear optimal control

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Contents

xv

20.9 Exercises
20.10 References

850
853

A

Basic Concepts and Definitions
A.l
Set theory and notation
A.2
Mappings and functions
A.3
Convex functions
A.4
О and о Notation
A.5
Continuity
A.6
Differentiation
A.6.1 Differentiation with a single real variable
A.6.2 Partial derivatives and gradients on W"
A.6.3 Linear approximation using the gradient
A.6.4 Taylor series
A.7
Basic constrained optimization
A.8
The Holder and Minkowski inequalities
A.9
Exercises
A. 10 References

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В

Completing the Square
B. 1
The scalar case
B.2
The matrix case
B.3
Exercises

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879

С

Basic Matrix Concepts
C.l
Notational conventions
C.2
Matrix Identity and Inverse
C.3
Transpose and trace
C.4
Block (partitioned) matrices
C.5
Determinants
C.5.1 Basic properties of determinants
C.5.2 Formulas for the determinant
C.5.3 Determinants and matrix inverses
C.6
Exercises
C.7
References

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890

D

Random Processes
D.l
Definitions of means and correlations
г D.2
Stationarity
D.3
Power spectral-density functions
D.4
Linear systems with stochastic inputs
D.4.1 Continuous-time signals and systems
D.4.2 Discrete-time signals and systems
D.5
References

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E

Derivatives and Gradients
E. 1 Derivatives of vectors and scalars with respect to a real vector
E.l.l Some important gradients
E.2 Derivatives of real-valued functions of real matrices
E.3 Derivatives of matrices with respect to scalars, and vice versa
E.4 The transformation principle
E.5 Derivatives of products of matrices

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xvi

Contents

E.6
E.7
E.8
E.9
E.10
F

Derivatives of powers of a matrix
Derivatives involving the trace
Modifications for derivatives of complex vectors and matrices
Exercises
References

904
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910
912

Conditional Expectations of Multinomial and Poisson r.v.s
F. 1 Multinomial distributions
F.2 Poisson random variables
F.3 Exercises

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914
914

Bibliography

915

Index

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&



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