Fundamentals of adaptive signal processing:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cham [u.a.]
Springer
2015
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| Series: | Signal and communication technology
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXV, 704 S. Ill., graph. Darst. |
| ISBN: | 9783319028064 |
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MARC
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| 100 | 1 | |a Uncini, Federico |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Fundamentals of adaptive signal processing |c Aurelio Uncini |
| 264 | 1 | |a Cham [u.a.] |b Springer |c 2015 | |
| 300 | |a XXV, 704 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| _version_ | 1804152187474935808 |
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| adam_text | Contents^
Discrete-Time Signals and Circuits Fundamentals
.............. 1
1.1
Introduction
......................................... 1
1.1.1
Discrete-Time Signals
............................ 2
1.1.2
Deterministic and Random Sequences
................ 3
1.2
Basic Deterministic Sequences
........................... 3
1.2.1
Unitary Impulse
................................ 3
1.2.2
Unit Step
..................................... 4
1.2.3
Real and Complex Exponential Sequences
............. 5
1.3
Discrete-Time Signal Representation with Unitary
Transformations
...................................... 5
1.3.1
The Discrete Fourier Transform
..................... 8
1.3.2
DFT as Unitary Transformation
..................... 8
1.3.3
Discrete Hartley Transform
........................ 9
1.3.4
Discrete Sine and Cosine Transforms
................. 10
1.3.5
Haar
Unitary Transform
.......................... 11
1.3.6
Data-Dependent Unitary Transformation
.............. 12
1.3.7
Orthonormal
Expansion of Signals: Mathematical
Foundations and Definitions
....................... 14
1.4
Discrete-Time Circuits
................................. 18
1.4.1
General Properties of DT Circuits
................... 21
1.4.2
Impulse Response
............................... 23
1.4.3
Properties of DT LTI Circuits
...................... 23
1.4.4
Elements Definition in DT Circuits
.................. 25
1.4.5
DT Circuits Representation in the Frequency Domain
..... 27
1.5
DT Circuits, Represented in the Transformed Domains
......... 30
1.5.1
The z-Transform
................................ 30
1.5.2
Discrete-Time Fourier Transform
.................... 31
1.5.3
The z-Domain Transfer Function and Relationship with
DTFT
........................................ 36
1.5.4
The DFT and z-Transform
......................... 36
XIX
xx Contents
1.6 DT
Circuits
Defined by Finite Difference Equations
........... 37
1.6.1
Pole-Zero Plot and Stability Criterion
................ 38
1.6.2
Circuits with the Impulse Response of Finite and
Infinite Duration
................................ 41
1.6.3
Example of FIR Filter
—
The Moving Average Filter
...... 44
1.6.4
Generalized Linear-Phase FIR Filters
................. 46
1.6.5
Example of IIR Filter
............................ 47
1.6.6
Inverse Filters
.................................. 53
References
............................................. 54
2
Introduction to Adaptive Signal and Array Processing
........... 55
2.1
Introduction
......................................... 55
2.1.1
Linear Versus Nonlinear Numerical Filter
............. 56
2.2
Definitions and Basic Property of Adaptive Filtering
........... 57
2.2.1
Adaptive Filter Classification
....................... 60
2.3
Main Adaptive Filtering Applications
...................... 66
2.3.1
Dynamic Physical System Identification Process
......... 66
2.3.2
Prediction
..................................... 68
2.3.3
Adaptive Inverse Modeling Estimation
................ 68
2.3.4
Adaptive Interference Cancellation
.................. 72
2.4
Array of Sensors and Array Processing
..................... 77
2.4.1
Multichannel Noise Cancellation and Estimation of
the Direction of Arrival
........................... 78
2.4.2
Beamforming
.................................. 78
2.4.3
Room Acoustics Active Control
..................... 81
2.5
Biological Inspired Intelligent Circuits
..................... 82
2.5.1
The Formal Neuron
.............................. 84
2.5.2
ANN Topology
................................. 84
2.5.3
Learning Algorithms Paradigms
..................... 85
2.5.4
Blind Signal Processing and Signal Source Separation
.... 86
References
............................................. 89
3
Optimal Linear Filter Theory
.............................. 91
3.1
Introduction
......................................... 91
3.2
Adaptive Filter Basic and Notations
....................... 91
3.2.1
The Linear Adaptive Filter
........................ 92
3.2.2
Composite Notations for Multiple-Input Multiple-Output
Filter
........................................ 94
3.2.3
Optimization Criterion and Cost Functions Definition
..... 99
3.2.4
Approximate Stochastic Optimization
................ 100
3.3
Adaptation By Stochastic Optimization
..................... 101
3.3.1
Normal Equations in
Wiener-Hopf
Notation
........... 102
3.3.2
On the Estimation of the Correlation Matrix
............ 105
3.3.3
Frequency Domain Interpretation and Coherence
Function
...................................... 108
3.3.4
Adaptive Filter Performance Measurement
............. 110
Contents xxi
3.3.5
Geometrical
Interpretation
and Orthogonality Principle
.... 113
3.3.6
Principal Component Analysis of Optimal Filter
......... 114
3.3.7
Complex Domain Extension of the Wiener Filter
........ 118
3.3.8
Multichannel Wiener s Normal Equations
............. 119
3.4
Examples of Applications
............................... 121
3.4.1
Dynamical System Modeling
1..................... 121
3.4.2
Dynamical System Modeling
2..................... 123
3.4.3
Time Delay Estimation
........................... 126
3.4.4
Communication Channel Equalization
................ 127
3.4.5
Adaptive Interference or Noise Cancellation
............ 131
3.4.6
AIC in Acoustic Underwater Exploration
.............. 138
3.4.7
AIC Without Secondary Reference Signal
............. 139
References
............................................. 141
4
Least Squares Method
................................... 143
4.1
Introduction
......................................... 143
4.1.1
The Basic Principle of Least Squares Method
........... 143
4.2
Least Squares Methods as Approximate Stochastic Optimization
. . . 144
4.2.1
Derivation of LS Method
.......................... 145
4.2.2
Adaptive Filtering Formulation with LS Method
......... 146
4.2.3
Implementing Notes and Time Indices
................ 151
4.2.4
Geometric Interpretation and Orthogonality Principle
..... 156
4.2.5
LS Variants
................................... 159
4.3
On the Solution of Linear Systems with LS Method
............ 169
4.3.1
About the Over and Underdetermined Linear Equations
Systems
...................................... 169
4.3.2
Iterative LS System Solution with Lyapunov Attractor
. ... 171
4.4
LS Methods Using Matrix Factorization
.................... 174
4.4.1
LS Solution by Cholesky Decomposition
.............. 175
4.4.2
LS Solution Methods with Orthogonalization
........... 177
4.4.3
LS Solution with the Singular Value Decomposition
Method
....................................... 180
4.5
Total Least Squares
................................... 184
4.5.1
TLS Solution
.................................. 186
4.5.2
Generalized TLS
................................ 188
4.6
Underdetermined Linear Systems with Sparse Solution
......... 190
4.6.1
The Matching Pursuit Algorithms
................... 191
4.6.2
Approximate Minimum L^-Norm LS Iterative Solution
.... 193
References
............................................. 203
5
First-Order Adaptive Algorithms
........................... 205
5.1
Introduction
......................................... 205
5.1.1
On the Recursive Formulation of the Adaptive
Algorithms
.................................... 206
5.1.2
Performance of Adaptive Algorithms
................. 214
5.1.3
General Properties of the Adaptation Algorithms
........ 220
xxii Contents
5.2
Method of Descent Along the Gradient: The Steepest-Descent
Algorithm
.......................................... 225
5.2.1
Multichannel Extension of the SDA
.................. 226
5.2.2
Convergence and Stability of the SDA
................ 227
5.2.3
Convergence Speed: Eigenvalues Disparities and
Nonuniform
Convergence
......................... 229
5.3
First-Order Stochastic-Gradient Algorithm: The Least Mean
Squares
............................................ 233
5.3.1
Formulation of the LMS Algorithm
.................. 233
5.3.2
Minimum Perturbation Properties and Alternative
LMS Algorithm Derivation
........................ 236
5.3.3
Extending LMS in the Complex Domain
.............. 237
5.3.4
LMS with Linear Constraints
....................... 239
5.3.5
Multichannel LMS Algorithms
..................... 242
5.4
Statistical Analysis and Performance of the LMS Algorithm
..... 246
5.4.1
Model for Statistical Analysis of the Adaptive
Algorithms Performance
.......................... 246
5.4.2
LMS Characterization and Convergence with
Stochastic Difference Equation
..................... 248
5.4.3
Excess of Error and Learning Curve
.................. 254
5.4.4
Convergence Speed: Eigenvalues Disparity and
Nonuniform
Convergence
......................... 258
5.4.5
Steady-State Analysis for Deterministic Input
........... 260
5.5
LMS Algorithm Variants
............................... 262
5.5.1
Normalized LMS Algorithm
....................... 262
5.5.2
Proportionate LMS Algorithms
..................... 265
5.5.3
Leaky LMS
................................... 267
5.5.4
Other Variants of the LMS Algorithm
................ 268
5.5.5
Delayed Learning LMS Algorithms
.................. 272
References
............................................. 284
6
Second-Order Adaptive Algorithms
......................... 287
6.1
Introduction
......................................... 287
6.2
Newton s Method and Error Sequential Regression Algorithms
.... 288
6.2.1
Newton s Algorithm
............................. 288
6.2.2
The Class of Error Sequential Regression Algorithms
..... 290
6.2.3
LMS-Newton Algorithm
.......................... 293
6.2.4
Recursive Estimation of the Time-Average Autocorrelation
. . . 294
6.3 Affine
Projection Algorithms
............................ 295
6.3.1
APA
Derivation Through Minimum Perturbation Property
. . . 296
6.3.2
Computational Complexity of
APA
.................. 298
6.3.3
The
APA
Class
................................. 299
6.4
The Recursive Least Squares
............................ 300
6.4.1
Derivation of the RLS Method
...................... 300
6.4.2
Recursive Class of the Correlation Matrix with
Forgetting Factor and
Kalman
Gain
.................. 301
Contents xxiii
6.4.3 RLS Update
with
A
Priori and
A
Posteriori
Error
........ 303
6.4.4
Conventional
RLS
Algorithm......................
305
6.4.5 Performance
Analysis and Convergence of RLS
......... 308
6.4.6
Nonstationary RLS Algorithm
...................... 314
6.5
Kalman
Filter
....................................... 315
6.5.1
Discrete-Time
Kalman
Filter Formulation
............. 316
6.5.2
The
Kalman
Filter Algorithm
....................... 320
6.5.3
Kalman
Filtering as an Extension of the RLS Criterion
.... 322
6.5.4
Kalman
Filter Robustness
......................... 323
6.5.5
KF Algorithm in the Presence of an External Signal
...... 323
6.6
Tracking Performance of Adaptive Algorithms
............... 324
6-6.1
Tracking Analysis Model
......................... 325
6.6.2
Performance Analysis Indices and Fundamental
Relationships
.................................. 327
6.6.3
Tracking Performance of LMS Algorithm
............. 330
6.6.4
RLS Performance in Nonstationary Environment
........ 332
6.7
MIMO
Error Sequential Regression Algorithms
............... 334
6.7.1
MIMO RLS
................................... 334
6.7.2
Low-Diversity Inputs
MIMO
Adaptive Filtering
......... 335
6.7.3
Multi-channel
APA
Algorithm
...................... 338
6.8
General Adaptation Law
................................ 339
6.8.1
Adaptive Regularized Form, with Sparsity Constraints
.... 340
6.8.2
Exponentiated Gradient Algorithms Family
............ 344
References
............................................. 348
7
Block and Transform Domain Algorithms
.................... 351
7.1
Introduction
......................................... 351
7.1.1
Block, Transform Domain, and Online Algorithms
Classification
.................................. 353
7.2
Block Adaptive Filter
.................................. 355
7.2.1
Block LMS Algorithm
............................ 357
7.2.2
Convergence Properties of BLMS
................... 358
7.3
Frequency Domain Block Adaptive Filtering
................. 358
7.3.1
Linear Convolution and Filtering in the Frequency
Domain
...................................... 359
7.3.2
Introduction of the FDAF
......................... 363
7.3.3
Overlap-Save FDAF Algorithm
..................... 365
7.3.4
UFDAF Algorithm
.............................. 368
7.3.5
Overlap-Add FDAF Algorithm
..................... 370
7.3.6
Overlap-Save FDAF Algorithm with Frequency
Domain Error
.................................. 371
7.3.7
UFDAF with
N =
M: Circular Convolution Method
...... 372
7.3.8
Performance Analysis of FDAF Algorithms
............ 376
7.4
Partitioned Impulse Response FDAF Algorithms
.............. 379
7.4.1
The Partitioned Block FDAF
....................... 379
xxiv Contents
7.4.2
Computational Cost of the PBFDAF
................. 385
7.4.3
PFDAF Algorithm Performance
..................... 386
7.5
Transform-Domain Adaptive Filters
....................... 388
7.5.1
TDAF Algorithms
............................... 388
7.5.2
Sliding Transformation LMS as Sampling Frequency
Interpretation with Bandpass Filters Bank
.............. 392
7.5.3
Performance of TDAF
............................ 399
7.6
Subband Adaptive Filtering
............................. 401
7.6.1
On the Subband-Coding Systems
.................... 401
7.6.2
Two-Channel Filter Banks
......................... 402
7.6.3
Open-Loop and Closed-Loop
SAF
................... 409
7.6.4
Circuit Architectures for
SAF
...................... 413
7.6.5
Characteristics of Analysis-Synthesis Filter Banks
in the
SAF
Structure
............................. 418
References
............................................. 419
8
Linear Prediction and Recursive Order Algorithms
............. 423
8.1
Introduction
......................................... 423
8.2
Linear Estimation: Forward and Backward Prediction
.......... 424
8.2.1
Wiener s Optimum Approach to the Linear Estimation
and Linear Prediction
............................ 424
8.2.2
Forward and Backward Prediction Using LS Approach
.... 435
8.2.3
Augmented Yule-Walker Normal Equations
........... 437
8.2.4
Spectral Estimation of a Linear Random Sequence
....... 439
8.2.5
Linear Prediction Coding of Speech Signals
............ 440
8.3
Recursive in Model Order Algorithms
...................... 442
8.3.1
Partitioned Matrix Inversion Lemma
................. 443
8.3.2
Recursive Order Adaptive Filters
.................... 445
8.3.3
Levinson—
Durbin
Algorithm
....................... 447
8.3.4
Lattice Adaptive Filters and Forward—Backward
Linear Prediction
............................... 453
8.3.5
Lattice as Orthogonalized Transform: Batch Joint
Process Estimation
.............................. 456
8.3.6
Gradient Adaptive Lattice Algorithm: Online Joint
Process Estimation
.............................. 459
8.3.7 Schür
Algorithm
................................ 463
8.3.8
All-Pole Inverse Lattice Filter
...................... 464
8.4
Recursive Order RLS Algorithms
......................... 465
8.4.1
Fast Fixed-Order RLS in ROF Formulation
............ 465
8.4.2
Algorithms
FKA,
FAEST, and FTF
.................. 470
References
................................ 475
9
Discrete Space-Time Filtering
.............................. 477
9.1
Introduction
............................... 477
9.1.1
Array Processing Applications
...................... 478
9Л.2
Types of Sensors
.............................................. 478
Contents xxv
9.1.3
Spatial
Sensors Distribution........................ 479
9.1.4 AP
Algorithms
................................. 480
9.2
Array
Processing Model and Notation...................... 481
9.2.1
Propagation
Model.............................. 481
9.2.2 Signal Model.................................. 486
9.2.3
Steering Vector for Typical
АР
Geometries
............ 489
9.2.4
Circuit Model for AP and Space-Time Sampling
........ 493
9.3
Noise Field Characteristics and Quality Indices
............... 498
9.3.1
Spatial Covariance Matrix and Projection Operators
...... 498
9.3.2
Noise Field Characteristics
........................ 501
9.3.3
Quality Indexes and Array Sensitivity
................ 504
9.4
Conventional Beamforming
............................. 511
9.4.1
Conventional Beamforming: DSBF-ULA
.............. 511
9.4.2
Differential Sensors Array
......................... 516
9.4.3
Broadband Beamformer with Spectral Decomposition
..... 522
9.4.4
Spatial Response Direct Synthesis with Approximate
Methods
...................................... 523
9.5
Data-Dependent Beamforming
........................... 527
9.5.1
Maximum
SNR
and
Superdirective
Beamformer
......... 528
9.5.2
Post-filtering Beamformer
......................... 534
9.5.3
Minimum Variance Broadband Beamformer: Frost
Algorithm
..................................... 537
9.6
Adaptive Beamforming with Sidelobe Canceller
.............. 547
9.6.1
Introduction to Adaptive Beamforming: The Multiple
Adaptive Noise Canceller
......................... 547
9.6.2
Generalized Sidelobe Canceller
..................... 547
9.6.3
GSC Adaptation
................................ 551
9.6.4
Composite-Notation GSC with
/
constraints
............ 554
9.6.5
Frequency Domain GSC
.......................... 556
9.6.6
Robust GSC Beamforming
........................ 558
9.6.7
Beamforming in High Reverberant Environment
......... 559
9.7
Direction of Arrival and Time Delay Estimation
.............. 561
9.7.1
Narrowband
DOA
............................... 561
9.7.2
Broadband
DOA
................................ 568
9.7.3
Time Delay Estimation Methods
.................... 569
References
............................................. 576
Appendix A: Linear Algebra Basics
............................ 579
Appendix B: Elements of Nonlinear Programming
................ 603
Appendix C: Elements of Random Variables, Stochastic Processes,
and Estimation Theory
........................... 633
References
............................................... 689
Index
................................................... 691
|
| any_adam_object | 1 |
| author | Uncini, Federico |
| author_facet | Uncini, Federico |
| author_role | aut |
| author_sort | Uncini, Federico |
| author_variant | f u fu |
| building | Verbundindex |
| bvnumber | BV041844478 |
| classification_rvk | ZN 6050 |
| ctrlnum | (OCoLC)903050371 (DE-599)BVBBV041844478 |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
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| id | DE-604.BV041844478 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:06:44Z |
| institution | BVB |
| isbn | 9783319028064 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027289078 |
| oclc_num | 903050371 |
| open_access_boolean | |
| owner | DE-29T DE-703 |
| owner_facet | DE-29T DE-703 |
| physical | XXV, 704 S. Ill., graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| series2 | Signal and communication technology |
| spelling | Uncini, Federico Verfasser aut Fundamentals of adaptive signal processing Aurelio Uncini Cham [u.a.] Springer 2015 XXV, 704 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Signal and communication technology Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Adaptivregelung (DE-588)4000457-0 gnd rswk-swf Adaptive Signalverarbeitung (DE-588)4128146-9 gnd rswk-swf Nachrichtenübertragungstechnik (DE-588)4139364-8 gnd rswk-swf Adaptive Signalverarbeitung (DE-588)4128146-9 s DE-604 Signalverarbeitung (DE-588)4054947-1 s Adaptivregelung (DE-588)4000457-0 s Nachrichtenübertragungstechnik (DE-588)4139364-8 s Erscheint auch als Online-Ausgabe 978-3-319-02807-1 Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027289078&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Uncini, Federico Fundamentals of adaptive signal processing Signalverarbeitung (DE-588)4054947-1 gnd Adaptivregelung (DE-588)4000457-0 gnd Adaptive Signalverarbeitung (DE-588)4128146-9 gnd Nachrichtenübertragungstechnik (DE-588)4139364-8 gnd |
| subject_GND | (DE-588)4054947-1 (DE-588)4000457-0 (DE-588)4128146-9 (DE-588)4139364-8 |
| title | Fundamentals of adaptive signal processing |
| title_auth | Fundamentals of adaptive signal processing |
| title_exact_search | Fundamentals of adaptive signal processing |
| title_full | Fundamentals of adaptive signal processing Aurelio Uncini |
| title_fullStr | Fundamentals of adaptive signal processing Aurelio Uncini |
| title_full_unstemmed | Fundamentals of adaptive signal processing Aurelio Uncini |
| title_short | Fundamentals of adaptive signal processing |
| title_sort | fundamentals of adaptive signal processing |
| topic | Signalverarbeitung (DE-588)4054947-1 gnd Adaptivregelung (DE-588)4000457-0 gnd Adaptive Signalverarbeitung (DE-588)4128146-9 gnd Nachrichtenübertragungstechnik (DE-588)4139364-8 gnd |
| topic_facet | Signalverarbeitung Adaptivregelung Adaptive Signalverarbeitung Nachrichtenübertragungstechnik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027289078&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT uncinifederico fundamentalsofadaptivesignalprocessing |