Adaptive filters:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[Piscataway, NJ u.a.]
IEEE Press [u.a.]
2008
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXX, 786 S. graph. Darst. |
| ISBN: | 9780470253885 |
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MARC
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| 020 | |a 9780470253885 |9 978-0-470-25388-5 | ||
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| 084 | |a ZN 5740 |0 (DE-625)157479: |2 rvk | ||
| 100 | 1 | |a Sayed, Ali H. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Adaptive filters |c Ali H. Sayed |
| 264 | 1 | |a [Piscataway, NJ u.a.] |b IEEE Press [u.a.] |c 2008 | |
| 300 | |a XXX, 786 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Filtres adaptatifs | |
| 650 | 4 | |a Adaptive filters | |
| 650 | 0 | 7 | |a Adaptives Filter |0 (DE-588)4141377-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Adaptives Filter |0 (DE-588)4141377-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391150&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016391150 | ||
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| _version_ | 1804137483557928960 |
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| adam_text | Preface
xvii
Notation xxv
Acknowledgments
xxx
BACKGROUND MATERIAL
A Random Variables
1
A.
1
Variance of a Random Variable
1
A.2 Dependent Random Variables
3
A.3 Complex-Valued Random Variables
4
A.4 Vector-Valued Random Variables
6
A.5 Gaussian Random Vectors
7
В
Linear Algebra
12
B.I Hermitian and Positive-Definite Matrices
12
B.2 Range Spaces and
Nullspaces
of Matrices
14
B.3
Schur
Complements
16
B.4 Cholesky Factorization
17
B.5 QR Decomposition
19
B.6 Singular Value Decomposition
20
B.7 Kronecker Products
23
С
Complex Gradients
25
C.I Cauchy-Riemann Conditions
25
C.2 Scalar Arguments
26
C.3 Vector Arguments
26
PARTI: OPTIMAL ESTIMATION
1
Scalar-Valued Data
29
1.1
Estimation Without Observations
29
1.2
Estimation Given Dependent Observations
31
vii
vl
1.3
Orthogonality Principle
36
contents 14 Gaussian Random Variables
38
2
Vector-Valued Data
42
2.1
Optimal Estimator in the Vector Case
42
2.2
Spherically Invariant Gaussian Variables
46
2.3
Equivalent Optimization Criterion
49
Summary and Notes
51
Problems and Computer Projects
54
PART II: LINEAR ESTIMATION
3
Normal Equations
60
3.1
Mean-Square Error Criterion
61
3.2
Mimmization by Differentiation
63
3.3
Minimization by Completion-of-Squares
63
3.4
Minimization of the Error Covariance Matrix
65
3.5
Optimal Linear Estimator
66
4
Orthogonality Principle
67
4.1
Design Examples
67
4.2
Orthogonality Condition
72
4.3
Existence of Solutions
74
4.4
Nonzero-Mean Variables
76
5
Linear Models
78
5.1
Estimation using Linear Relations
78
5.2
Application: Channel Estimation
80
5.3
Application: Block Data Estimation
81
5.4
Application: Linear Channel Equalization
82
5.5
Application: Multiple-Antenna Receivers
85
6
Constrained Estimation
87
6.1
Minimum-Variance Unbiased Estimation
88
6.2
Example: Mean Estimation
90
6.3
Application: Channel and Noise Estimation
91
6.4
Application: Decision Feedback Equalization
93
6.5
Application: Antenna Beamforming
101
7 Kaiman
Filter
104
7.1
Innovations Process
104
7.2
State-Space Model
106
7.3
Recursion for the State Estimator
7.4
Computing the Gain Matrix
7.5
Riccati Recursion
7.6
Covariance Form
7.7
Measurement and Time-Update Form
Summary and Notes
Problems and Computer Projects
107
ix
108
CONTENTS
109
109
110
111
115
PART III
:
STOCHASTIC GRADIENT ALGORITHMS
8
Steepest-Descent Technique
139
8.1
Linear Estimation Problem
140
8.2
Steepest-Descent Method
142
8.3
More General Cost Functions
147
9
Transient Behavior
148
9.1
Modes of Convergence
148
9.2
Optimal Step-Size
149
9.3
Weight-Error Vector Convergence
151
9.4
Time Constants
153
9.5
Learning Curve
154
9.6
Contour Curves of the Error Surface
155
9.7
Iteration-Dependent Step-Sizes
157
9.8
Newton s Method
160
10
LMS Algorithm
163
10.1
Motivation
163
10.2
Instantaneous Approximation
165
10.3
Computational Cost
166
10.4
Least-Perturbation Property
167
10.5
Application: Adaptive Channel Estimation
168
10.6
Application: Adaptive Channel Equalization
171
10.7
Application: Decision-Feedback Equalization
172
10.8
Ensemble-Average Learning Curves
174
11
Normalized LMS Algorithm
178
11.1
Instantaneous Approximation
178
11.2
Computational Cost
179
11.3
Power Normalization
180
11.4
Least-Perturbation Property
182
12
Other LMS-Type Algorithms
183
12.1
Non-Blind Algorithms
183
CONTENTS 122 Blind Algorithms 186
12.3
Some Properties
188
13 Affine
Projection Algorithm
191
13.1
Instantaneous Approximation
191
13.2
Computational Cost
193
13.3
Least-Perturbation Property
193
13.4 Affine
Projection Interpretation
194
14
RLS Algorithm
198
14.1
Instantaneous Approximation
198
14.2
Computational Cost
200
Summary and Notes
202
Problems and Computer Projects
209
PART IV: MEAN-SQUARE PERFORMANCE
15
Energy Conservation
228
15.1
Performance Measure
228
15.2
Stationary Data Model
230
15.3
Energy Conservation Relation
234
15.4
Variance Relation
237
15.
A Interpretations of the Energy Relation
239
16
Performance of LMS
244
16.1
Variance Relation
244
16.2
Small Step-Sizes
245
16.3
Separation Principle
245
16.4
White Gaussian Input
246
16.5
Statement of Results
249
16.6
Simulation Results
250
17
Performance of NLMS
252
17.1
Separation Principle
252
17.2
Simulation Results
254
17.A Relating NLMS to LMS
254
18
Performance of Sign-Error LMS
257
18.1
Real-Valued Data
257
18.2
Complex-Valued Data
259
18.3
Simulation Results
260
19
Performance
of RLS and Other
Filters
262
x
CONTENTS
19.1
Performance of RLS
262
19.2
Performance of Other Filters
266
19.3
Performance Table for Small Step-Sizes
269
20
Nonstationary Environments
270
20.1
Motivation
270
20.2
Nonstationary Data Model
271
20.3
Energy Conservation Relation
276
20.4
Variance Relation
277
21
Tracking Performance
280
21.1
Performance of LMS
280
21.2
Performance of NLMS
284
21.3
Performance of Sign-Error LMS
285
21.4
Performance of RLS
287
21.5
Comparison of Tracking Performance
289
21.6
Comparing RLS and LMS
292
21.7
Performance of Other Filters
293
21.8
Performance Table for Small Step-Sizes
295
Summary and Notes
296
Problems and Computer Projects
304
PART V: TRANSIENT PERFORMANCE
22
Weighted Energy Conservation
329
22.1
Data Model
329
22.2
Data-Normalized Adaptive Filters
330
22.3
Weighted Energy Conservation Relation
330
22.4
Weighted Variance Relation
333
23
LMS with Gaussian Regressors
340
23.1
Mean and Variance Relations
340
23.2
Mean Behavior
343
23.3
Mean-Square Behavior
343
23.4
Mean-Square Stability
346
23.5
Steady-State Performance
350
23.6
Small Step-Size Approximations
353
23.A Convergence Time
353
24
LMS with non-Gaussian Regressors
357
24.1
Mean and Variance Relations
357
xil
24.2
Mean-Square Stability and Performance
360
contents
24 3
SmaU Step-Size Approximations
362
24.A Independence and Averaging Analysis
363
25
Data-Normalized Filters
371
25.1
NLMS Filter
371
25.2
Data-Normalized Filters
374
25.A Stability Bound
377
25.B Stability of NLMS
378
Summary and Notes
380
Problems and Computer Projects
388
PART VI: BLOCK ADAPTIVE FILTERS
26
Transform Domain Adaptive Filters
413
26.1
Transform-Domain Filters
413
26.2
DFT-Domain LMS
421
26.3
DCT-Domain LMS
423
26.A DCT-Transformed Regressors
424
27
Efficient Block Convolution
426
27.1
Motivation
426
27.2
Block Data Formulation
428
27.3
Block Convolution
431
28
Block and Subband Adaptive Filters
440
28.1
DFT Block Adaptive Filters
440
28.2
Subband Adaptive Filters
447
28.A Another Constrained DFT Block Filter
453
28.B Overlap-Add Block Adaptive Filters
455
Summary and Notes
465
Problems and Computer Projects
468
PART
VII:
LEAST-SQUARES METHODS
29
Least-Squares Criterion
476
29.1
Least-Squares Problem
477
29.2
Geometric Argument
478
29.3
Algebraic Arguments
480
29.4
Properties of Least-Squares Solution
482
29.5
Projection Matrices
484
29.6
Weighted Least-Squares
485
χίίί
29.7
Regularized Least-Squares
487
contents
29.8
Weighted Regularized Least-Squares
489
30
Recursive Least-Squares
492
30.1
Motivation
492
30.2
RLS Algorithm
493
30.3
Regularization
495
30.4
Conversion Factor
496
30.5
Time-Update of the Minimum Cost
497
30.6
Exponentially-Weighted RLS Algorithm
498
31
Kalman
Filtering and RLS
501
31.1
Equivalence in Linear Estimation
501
31.2
Kalman
Filtering and Recursive Least-Squares
502
31.
A Extended RLS Algorithms
508
32
Order and Time-Update Relations
515
32.1
Backward Order-Update Relations
515
32.2
Forward Order-Update Relations
525
32.3
Time-Update Relation
529
Summary and Notes
534
Problems and Computer Projects
541
PART
VIII:
ARRAY ALGORITHMS
33
Norm and Angle Preservation
561
33.1
Some Difficulties
561
33.2
Square-Root Factors
562
33.3
Preservation Properties
564
33.4
Motivation for Array Methods
566
34
Unitary Transformations
571
34.1
Givens
Rotations
571
34.2
Householder Transformations
576
35
QR and Inverse QR Algorithms
580
35.1
Inverse QR Algorithm
581
35.2
QR Algorithm
584
35.3
Extended QR Algorithm
588
35.A Array Algorithms for
Kalman
Filtering
589
Summary and Notes
593
xiv
Problems
and Computer Projects
595
CONTENTS
_____________
__^^^^^^^^__^^^^^__^^^^_^__^^^^^^^^_
PART IX: FAST RLS ALGORITHMS
36
Hyperbolic Rotations
602
36.1
Hyperbolic
Givens
Rotations
602
36.2
Hyperbolic Householder Transfonnations
605
36.3
Hyperbolic Basis Rotations
608
37
Fast Array Algorithm
610
37.1
Time-Update of the Gain Vector
611
37.2
Time-Update of the Conversion Factor
612
37.3
Initial Conditions
613
37.4
Array Algorithm
614
37.
A Chandrasekhar Filter
618
38
Regularized Prediction Problems
621
38.1
Regularized Backward Prediction
622
38.2
Regularized Forward Prediction
624
38.3
Low-Rank Factorization
627
39
Fast Fixed-Order Filters
628
39.1
Fast Transversal Filter
628
39.2
FAEST Filter
630
39.3
Fast
Kalman
Filter
631
39.4
Stability Issues
633
Summary and Notes
639
Problems and Computer Projects
642
PARTX: LATTICE FILTERS
40
Three Basic Estimation Problems
653
40.1
Motivation for Lattice Filters
654
40.2
Joint Process Estimation
656
40.3
Backward Estimation Problem
659
40.4
Forward Estimation Problem
662
40.5
Time and Order-Update Relations
664
41
Lattice Filter Algorithms
669
41.1
Significance of Data Structure
669
41.2
A Posteriori-Based Lattice Filter
672
41.3
A Priori-Based
Lattice
Filter
673
CONTENTS
42
Error-Feedback Lattice Filters
676
42.1
A Priori Error-Feedback Lattice Filter
676
42.2
A Posteriori Error-Feedback Lattice Filter
680
42.3
Normalized Lattice Filter
682
43
Array Lattice Filters
688
43.1
Order-Update of Output Estimation Errors
689
43.2
Order-Update of Backward Estimation Errors
690
43.3
Order-Update of Forward Estimation Errors
691
43.4
Significance of Data Structure
693
Summary and Notes
695
Problems and Computer Projects
698
PART XI: ROBUST FILTERS
44
Indefinite Least-Squares
705
44.1
Indefinite Least-Squares Formulation
705
44.2
Recursive Minimization Algorithm
710
44.3
Time-Update of the Minimum Cost
713
44.4
Singular Weighting Matrices
714
44.
A Stationary Points
716
44.B Inertia Conditions
716
45
Robust Adaptive Filters
718
45.1
A Posteriori-Based Robust Filters
718
45.2
e-NLMS Algorithm
724
45.3
A Priori-Based Robust Filters
726
45.4
LMS Algorithm
730
45.A
П°°
Filters
732
46
Robustness Properties
735
46.1
Robustness of LMS
735
46.2
Robustness of
e- N
LMS
739
46.3
Robustness of RLS
740
Summary and Notes
743
Problems and Computer Projects
747
REFERENCES AND INDICES
References
758
Author Index
775
Subject Index
780
|
| adam_txt |
Preface
xvii
Notation xxv
Acknowledgments
xxx
BACKGROUND MATERIAL
A Random Variables
1
A.
1
Variance of a Random Variable
1
A.2 Dependent Random Variables
3
A.3 Complex-Valued Random Variables
4
A.4 Vector-Valued Random Variables
6
A.5 Gaussian Random Vectors
7
В
Linear Algebra
12
B.I Hermitian and Positive-Definite Matrices
12
B.2 Range Spaces and
Nullspaces
of Matrices
14
B.3
Schur
Complements
16
B.4 Cholesky Factorization
17
B.5 QR Decomposition
19
B.6 Singular Value Decomposition
20
B.7 Kronecker Products
23
С
Complex Gradients
25
C.I Cauchy-Riemann Conditions
25
C.2 Scalar Arguments
26
C.3 Vector Arguments
26
PARTI: OPTIMAL ESTIMATION
1
Scalar-Valued Data
29
1.1
Estimation Without Observations
29
1.2
Estimation Given Dependent Observations
31
vii
vl"
1.3
Orthogonality Principle
36
contents 14 Gaussian Random Variables
38
2
Vector-Valued Data
42
2.1
Optimal Estimator in the Vector Case
42
2.2
Spherically Invariant Gaussian Variables
46
2.3
Equivalent Optimization Criterion
49
Summary and Notes
51
Problems and Computer Projects
54
PART II: LINEAR ESTIMATION
3
Normal Equations
60
3.1
Mean-Square Error Criterion
61
3.2
Mimmization by Differentiation
63
3.3
Minimization by Completion-of-Squares
63
3.4
Minimization of the Error Covariance Matrix
65
3.5
Optimal Linear Estimator
66
4
Orthogonality Principle
67
4.1
Design Examples
67
4.2
Orthogonality Condition
72
4.3
Existence of Solutions
74
4.4
Nonzero-Mean Variables
76
5
Linear Models
78
5.1
Estimation using Linear Relations
78
5.2
Application: Channel Estimation
80
5.3
Application: Block Data Estimation
81
5.4
Application: Linear Channel Equalization
82
5.5
Application: Multiple-Antenna Receivers
85
6
Constrained Estimation
87
6.1
Minimum-Variance Unbiased Estimation
88
6.2
Example: Mean Estimation
90
6.3
Application: Channel and Noise Estimation
91
6.4
Application: Decision Feedback Equalization
93
6.5
Application: Antenna Beamforming
101
7 Kaiman
Filter
104
7.1
Innovations Process
104
7.2
State-Space Model
106
7.3
Recursion for the State Estimator
7.4
Computing the Gain Matrix
7.5
Riccati Recursion
7.6
Covariance Form
7.7
Measurement and Time-Update Form
Summary and Notes
Problems and Computer Projects
107
ix
108
CONTENTS
109
109
110
111
115
PART III
:
STOCHASTIC GRADIENT ALGORITHMS
8
Steepest-Descent Technique
139
8.1
Linear Estimation Problem
140
8.2
Steepest-Descent Method
142
8.3
More General Cost Functions
147
9
Transient Behavior
148
9.1
Modes of Convergence
148
9.2
Optimal Step-Size
149
9.3
Weight-Error Vector Convergence
151
9.4
Time Constants
153
9.5
Learning Curve
154
9.6
Contour Curves of the Error Surface
155
9.7
Iteration-Dependent Step-Sizes
157
9.8
Newton's Method
160
10
LMS Algorithm
163
10.1
Motivation
163
10.2
Instantaneous Approximation
165
10.3
Computational Cost
166
10.4
Least-Perturbation Property
167
10.5
Application: Adaptive Channel Estimation
168
10.6
Application: Adaptive Channel Equalization
171
10.7
Application: Decision-Feedback Equalization
172
10.8
Ensemble-Average Learning Curves
174
11
Normalized LMS Algorithm
178
11.1
Instantaneous Approximation
178
11.2
Computational Cost
179
11.3
Power Normalization
180
11.4
Least-Perturbation Property
182
12
Other LMS-Type Algorithms
183
12.1
Non-Blind Algorithms
183
CONTENTS 122 Blind Algorithms 186
12.3
Some Properties
188
13 Affine
Projection Algorithm
191
13.1
Instantaneous Approximation
191
13.2
Computational Cost
193
13.3
Least-Perturbation Property
193
13.4 Affine
Projection Interpretation
194
14
RLS Algorithm
198
14.1
Instantaneous Approximation
198
14.2
Computational Cost
200
Summary and Notes
202
Problems and Computer Projects
209
PART IV: MEAN-SQUARE PERFORMANCE
15
Energy Conservation
228
15.1
Performance Measure
228
15.2
Stationary Data Model
230
15.3
Energy Conservation Relation
234
15.4
Variance Relation
237
15.
A Interpretations of the Energy Relation
239
16
Performance of LMS
244
16.1
Variance Relation
244
16.2
Small Step-Sizes
245
16.3
Separation Principle
245
16.4
White Gaussian Input
246
16.5
Statement of Results
249
16.6
Simulation Results
250
17
Performance of NLMS
252
17.1
Separation Principle
252
17.2
Simulation Results
254
17.A Relating NLMS to LMS
254
18
Performance of Sign-Error LMS
257
18.1
Real-Valued Data
257
18.2
Complex-Valued Data
259
18.3
Simulation Results
260
19
Performance
of RLS and Other
Filters
262
x'
CONTENTS
19.1
Performance of RLS
262
19.2
Performance of Other Filters
266
19.3
Performance Table for Small Step-Sizes
269
20
Nonstationary Environments
270
20.1
Motivation
270
20.2
Nonstationary Data Model
271
20.3
Energy Conservation Relation
276
20.4
Variance Relation
277
21
Tracking Performance
280
21.1
Performance of LMS
280
21.2
Performance of NLMS
284
21.3
Performance of Sign-Error LMS
285
21.4
Performance of RLS
287
21.5
Comparison of Tracking Performance
289
21.6
Comparing RLS and LMS
292
21.7
Performance of Other Filters
293
21.8
Performance Table for Small Step-Sizes
295
Summary and Notes
296
Problems and Computer Projects
304
PART V: TRANSIENT PERFORMANCE
22
Weighted Energy Conservation
329
22.1
Data Model
329
22.2
Data-Normalized Adaptive Filters
330
22.3
Weighted Energy Conservation Relation
330
22.4
Weighted Variance Relation
333
23
LMS with Gaussian Regressors
340
23.1
Mean and Variance Relations
340
23.2
Mean Behavior
343
23.3
Mean-Square Behavior
343
23.4
Mean-Square Stability
346
23.5
Steady-State Performance
350
23.6
Small Step-Size Approximations
353
23.A Convergence Time
353
24
LMS with non-Gaussian Regressors
357
24.1
Mean and Variance Relations
357
xil
24.2
Mean-Square Stability and Performance
360
contents
24 3
SmaU Step-Size Approximations
362
24.A Independence and Averaging Analysis
363
25
Data-Normalized Filters
371
25.1
NLMS Filter
371
25.2
Data-Normalized Filters
374
25.A Stability Bound
377
25.B Stability of NLMS
378
Summary and Notes
380
Problems and Computer Projects
388
PART VI: BLOCK ADAPTIVE FILTERS
26
Transform Domain Adaptive Filters
413
26.1
Transform-Domain Filters
413
26.2
DFT-Domain LMS
421
26.3
DCT-Domain LMS
423
26.A DCT-Transformed Regressors
424
27
Efficient Block Convolution
426
27.1
Motivation
426
27.2
Block Data Formulation
428
27.3
Block Convolution
431
28
Block and Subband Adaptive Filters
440
28.1
DFT Block Adaptive Filters
440
28.2
Subband Adaptive Filters
447
28.A Another Constrained DFT Block Filter
453
28.B Overlap-Add Block Adaptive Filters
455
Summary and Notes
465
Problems and Computer Projects
468
PART
VII:
LEAST-SQUARES METHODS
29
Least-Squares Criterion
476
29.1
Least-Squares Problem
477
29.2
Geometric Argument
478
29.3
Algebraic Arguments
480
29.4
Properties of Least-Squares Solution
482
29.5
Projection Matrices
484
29.6
Weighted Least-Squares
485
χίίί
29.7
Regularized Least-Squares
487
contents
29.8
Weighted Regularized Least-Squares
489
30
Recursive Least-Squares
492
30.1
Motivation
492
30.2
RLS Algorithm
493
30.3
Regularization
495
30.4
Conversion Factor
496
30.5
Time-Update of the Minimum Cost
497
30.6
Exponentially-Weighted RLS Algorithm
498
31
Kalman
Filtering and RLS
501
31.1
Equivalence in Linear Estimation
501
31.2
Kalman
Filtering and Recursive Least-Squares
502
31.
A Extended RLS Algorithms
508
32
Order and Time-Update Relations
515
32.1
Backward Order-Update Relations
515
32.2
Forward Order-Update Relations
525
32.3
Time-Update Relation
529
Summary and Notes
534
Problems and Computer Projects
541
PART
VIII:
ARRAY ALGORITHMS
33
Norm and Angle Preservation
561
33.1
Some Difficulties
561
33.2
Square-Root Factors
562
33.3
Preservation Properties
564
33.4
Motivation for Array Methods
566
34
Unitary Transformations
571
34.1
Givens
Rotations
571
34.2
Householder Transformations
576
35
QR and Inverse QR Algorithms
580
35.1
Inverse QR Algorithm
581
35.2
QR Algorithm
584
35.3
Extended QR Algorithm
588
35.A Array Algorithms for
Kalman
Filtering
589
Summary and Notes
593
xiv
Problems
and Computer Projects
595
CONTENTS
_
_^^^^^^^^_^^^^^_^^^^_^_^^^^^^^^_
PART IX: FAST RLS ALGORITHMS
36
Hyperbolic Rotations
602
36.1
Hyperbolic
Givens
Rotations
602
36.2
Hyperbolic Householder Transfonnations
605
36.3
Hyperbolic Basis Rotations
608
37
Fast Array Algorithm
610
37.1
Time-Update of the Gain Vector
611
37.2
Time-Update of the Conversion Factor
612
37.3
Initial Conditions
613
37.4
Array Algorithm
614
37.
A Chandrasekhar Filter
618
38
Regularized Prediction Problems
621
38.1
Regularized Backward Prediction
622
38.2
Regularized Forward Prediction
624
38.3
Low-Rank Factorization
627
39
Fast Fixed-Order Filters
628
39.1
Fast Transversal Filter
628
39.2
FAEST Filter
630
39.3
Fast
Kalman
Filter
631
39.4
Stability Issues
633
Summary and Notes
639
Problems and Computer Projects
642
PARTX: LATTICE FILTERS
40
Three Basic Estimation Problems
653
40.1
Motivation for Lattice Filters
654
40.2
Joint Process Estimation
656
40.3
Backward Estimation Problem
659
40.4
Forward Estimation Problem
662
40.5
Time and Order-Update Relations
664
41
Lattice Filter Algorithms
669
41.1
Significance of Data Structure
669
41.2
A Posteriori-Based Lattice Filter
672
41.3
A Priori-Based
Lattice
Filter
673
CONTENTS
42
Error-Feedback Lattice Filters
676
42.1
A Priori Error-Feedback Lattice Filter
676
42.2
A Posteriori Error-Feedback Lattice Filter
680
42.3
Normalized Lattice Filter
682
43
Array Lattice Filters
688
43.1
Order-Update of Output Estimation Errors
689
43.2
Order-Update of Backward Estimation Errors
690
43.3
Order-Update of Forward Estimation Errors
691
43.4
Significance of Data Structure
693
Summary and Notes
695
Problems and Computer Projects
698
PART XI: ROBUST FILTERS
44
Indefinite Least-Squares
705
44.1
Indefinite Least-Squares Formulation
705
44.2
Recursive Minimization Algorithm
710
44.3
Time-Update of the Minimum Cost
713
44.4
Singular Weighting Matrices
714
44.
A Stationary Points
716
44.B Inertia Conditions
716
45
Robust Adaptive Filters
718
45.1
A Posteriori-Based Robust Filters
718
45.2
e-NLMS Algorithm
724
45.3
A Priori-Based Robust Filters
726
45.4
LMS Algorithm
730
45.A
П°°
Filters
732
46
Robustness Properties
735
46.1
Robustness of LMS
735
46.2
Robustness of
e- N
LMS
739
46.3
Robustness of RLS
740
Summary and Notes
743
Problems and Computer Projects
747
REFERENCES AND INDICES
References
758
Author Index
775
Subject Index
780 |
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| author | Sayed, Ali H. |
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| illustrated | Illustrated |
| index_date | 2024-07-02T20:09:37Z |
| indexdate | 2024-07-09T21:13:01Z |
| institution | BVB |
| isbn | 9780470253885 |
| language | English |
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| spelling | Sayed, Ali H. Verfasser aut Adaptive filters Ali H. Sayed [Piscataway, NJ u.a.] IEEE Press [u.a.] 2008 XXX, 786 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Filtres adaptatifs Adaptive filters Adaptives Filter (DE-588)4141377-5 gnd rswk-swf Adaptives Filter (DE-588)4141377-5 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391150&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sayed, Ali H. Adaptive filters Filtres adaptatifs Adaptive filters Adaptives Filter (DE-588)4141377-5 gnd |
| subject_GND | (DE-588)4141377-5 |
| title | Adaptive filters |
| title_auth | Adaptive filters |
| title_exact_search | Adaptive filters |
| title_exact_search_txtP | Adaptive filters |
| title_full | Adaptive filters Ali H. Sayed |
| title_fullStr | Adaptive filters Ali H. Sayed |
| title_full_unstemmed | Adaptive filters Ali H. Sayed |
| title_short | Adaptive filters |
| title_sort | adaptive filters |
| topic | Filtres adaptatifs Adaptive filters Adaptives Filter (DE-588)4141377-5 gnd |
| topic_facet | Filtres adaptatifs Adaptive filters Adaptives Filter |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016391150&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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