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Digital Spectral Analysis with Applications: Second Edition: Second Edition nd Edition [Pehme köide]

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  • Formaat: Paperback / softback, 512 pages, kõrgus x laius x paksus: 228x152x21 mm, kaal: 585 g
  • Ilmumisaeg: 29-Mar-2019
  • Kirjastus: Dover Publications Inc.
  • ISBN-10: 048678052X
  • ISBN-13: 9780486780528
Teised raamatud teemal:
Digital Spectral Analysis with Applications: Second Edition: Second Edition nd EditionSuurem pilt
  • Formaat: Paperback / softback, 512 pages, kõrgus x laius x paksus: 228x152x21 mm, kaal: 585 g
  • Ilmumisaeg: 29-Mar-2019
  • Kirjastus: Dover Publications Inc.
  • ISBN-10: 048678052X
  • ISBN-13: 9780486780528
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780486780528.html
Märksõnad:
"The book is designed to provide a broad perspective of spectral estimation techniques and their implementation. Prerequisites are some knowledge of discrete-time linear system and transform theory, introductory probability and statistics, and linear algebra. The text provides the theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. Topics covered include classical spectral estimation, Prony's method, parametric methods, the minimum variance method, eigenanalysis-based estimators, multichannel methods, and two-dimensional methods"--

Digital Spectral Analysis offers a broad perspective of spectral estimation techniques and their implementation. Coverage includes spectral estimation of discrete-time or discrete-space sequences derived by sampling continuous-time or continuous-space signals. The treatment emphasizes the behavior of each spectral estimator for short data records and provides over 40 techniques described and available as implemented MATLAB functions.
In addition to summarizing classical spectral estimation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. Topics include Prony's method, parametric methods, the minimum variance method, eigenanalysis-based estimators, multichannel methods, and two-dimensional methods. Suitable for advanced undergraduates and graduate students of electrical engineering — and for scientific use in the signal processing application community outside of universities — the treatment's prerequisites include some knowledge of discrete-time linear system and transform theory, introductory probability and statistics, and linear algebra. 1987 edition.


Designed to offer a broad perspective on spectral estimations techniques and their implementation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. 1987 edition.
Notational Conventions ix
List of Key Symbols xi
MATLAB Software xv
Preface xix
1 Introduction 1(24)
1.1 Historical Perspective
3(8)
1.2 Sunspot Numbers
11(4)
1.3 A Test Case
15(1)
1.4 Issues in Spectral Estimation
16(4)
1.5 How to Use This Text
20(1)
References
20(5)
2 Review of Linear Systems and Transform Theory 25(28)
2.1 Introduction
25(1)
2.2 Signal Notation
26(1)
2.3 Continuous Linear Systems
26(2)
2.4 Discrete Linear Systems
28(2)
2.5 Continuous-Time Fourier Transform
30(3)
2.6 Sampling and Windowing Operations
33(3)
2.7 Relating the Continuous and Discrete Transforms
36(5)
2.8 The Issue of Scaling for Power Determination
41(1)
2.9 The Issue of Zero Padding
42(1)
2.10 The Fast Fourier Transform
43(2)
2.11 Resolution and the Time-Bandwidth Product
45(3)
2.12 Extra: Source of Complex-Valued Signals
48(2)
2.13 Extra: Wavenumber Processing with Linear Spatial Arrays
50(1)
References
50(3)
3 Review of Matrix Algebra 53(44)
3.1 Introduction
53(1)
3.2 Matrix Algebra Basics
53(4)
3.3 Special Vector and Matrix Structures
57(5)
3.4 Matrix Inverse
62(3)
3.5 Solution of Linear Equations
65(8)
3.6 Overdetermined and Underdetermined Linear Equations
73(3)
3.7 Solution of Overdetermined and Underdetermined Linear Equations
76(4)
3.8 The Toeplitz Matrix
80(13)
3.9 The Vandermonde Matrix
93(1)
References
94(3)
4 Review of Random Process Theory 97(20)
4.1 Introduction
97(1)
4.2 Probability and Random Variables
97(3)
4.3 Random Processes
100(7)
4.4 Substituting Time Averages for Ensemble Averages
107(4)
4.5 Entropy Concepts
111
4.6 Limit Spectra of Test Data I
12(100)
4.7 Extra: Bias and Variance of the Sample Spectrum
112(3)
References
115(2)
5 Classical Spectral Estimation 117(38)
5.1 Introduction
117(1)
5.2 Summary
118(4)
5.3 Windows
122(8)
5.4 Resolution and the Stability-Time-Bandwidth Product
130(2)
5.5 Autocorrelation and Cross Correlation Estimation
132(4)
5.6 Correlogram Method PSD Estimators
136(4)
5.7 Periodogram PSD Estimators
140(6)
5.8 Combined Periodogram/Correlogram Estimators
146(3)
5.9 Application to Sunspot Numbers
149(3)
5.10 Conclusion
152(1)
References
153(2)
6 Parametric Models of Random Processes 155(16)
6.1 Introduction
155(1)
6.2 Summary
156(1)
6.3 AR, MA, and ARMA Random Process Models
157(4)
6.4 Relationships Among AR, MA, and ARMA Process Parameters
161(3)
6.5 Relationship of AR, MA, and ARMA Parameters to the Autocorrelation Sequence
164(3)
6.6 Spectral Factorization
167(2)
References
169(2)
7 Autoregressive Process and Spectrum Properties 171(16)
7.1 Introduction
171(1)
7.2 Summary
171(1)
7.3 Autoregressive Process Properties
172(8)
7.4 Autoregressive Power Spectral Density Properties
180(5)
References
185(2)
8 Autoregressive Spectral Estimation: Block Data Algorithms 187(48)
8.1 Introduction
187(1)
8.2 Summary
188(3)
8.3 Correlation Function Estimation Method
191(1)
8.4 Reflection Coefficient Estimation Methods
191(6)
8.5 Least Squares Linear Prediction Estimation Methods
197(9)
8.6 Estimator Characteristics
206(5)
8.7 Model Order Selection
211(2)
8.8 Autoregressive Processes with Observation Noise
213(1)
8.9 Application to Sunspot Numbers
214(2)
8.10 Extra: Covariance Linear Prediction Fast Algorithm
216(8)
8.11 Extra: Modified Covariance Linear Prediction Fast Algorithm
224(7)
References
231(4)
9 Autoregressive Spectral Estimation: Sequential Data Algorithms 235(20)
9.1 Introduction
235(1)
9.2 Summary
236(1)
9.3 Gradient Adaptive Autoregressive Methods
237(3)
9.4 Recursive Least Squares (RLS) Autoregressive Methods
240(6)
9.5 Fast Lattice Autoregressive Methods
246(1)
9.6 Application to Sunspot Numbers
247(1)
9.7 Extra: Fast RLS Algorithm for Recursive Linear Prediction
248(5)
References
253(2)
10 Autoregressive Moving Average Spectral Estimation 255(16)
10.1 Introduction
255(1)
10.2 Summary
256(2)
10.3 Moving Average Parameter Estimation
258(2)
10.4 Separate Autoregressive and Moving Average Parameter Estimation
260(4)
10.5 Simultaneous Autoregressive and Moving Average Parameter Estimation
264(1)
10.6 Sequential Approach to ARMA Estimation
265(1)
10.7 Special ARMA Process for Sinusoids in White Noise
266(1)
10.8 Application to Sunspot Numbers
267(1)
References
268(3)
11 Prony's Method 271(28)
11.1 Introduction
271(1)
11.2 Summary
272(2)
11.3 Simultaneous Exponential Parameter Estimation
274(1)
11.4 Original Prony Concept
275(2)
11.5 Least Squares Prony Method
277(3)
11.6 Modified Least Squares Prony Method
280(3)
11.7 Prony Spectrum
283(3)
11.8 Accounting for Known Exponential Components
286(2)
11.9 Identification of Exponentials in Noise
288(4)
11.10 Application to Sunspot Numbers
292(1)
11.11 Extra: Fast Algorithm to Solve Symmetric Covariance Normal Equations
293(4)
References
297(2)
12 Minimum Variance Spectral Estimation 299(10)
12.1 Introduction
299(1)
12.2 Summary
299(2)
12.3 Derivation of the Minimum Variance Spectral Estimator
301(2)
12.4 Relationship of MV and AR Spectral Estimators
303(2)
12.5 Implementation of the Minimum Variance Spectral Estimator
305(2)
12.6 Application to Sunspot Numbers
307(1)
References
307(2)
13 Eigenanalysis-Based Frequency Estimation 309(16)
13.1 Introduction
309(1)
13.2 Summary
309(2)
13.3 Eigenanalysis of Autocorrelation Matrix for Sinusoids in White Noise
311(3)
13.4 Eigenanalysis of Data Matrix for Exponentials in Noise
314(2)
13.5 Signal Subspace Frequency Estimators
316(3)
13.6 Noise Subspace Frequency Estimators
319(4)
13.7 Order Selection
323(1)
References
323(2)
14 Summary of Spectral Estimators 325(6)
References
330(1)
15 Multichannel Spectral Estimation 331(38)
15.1 Introduction
331(1)
15.2 Summary
331(1)
15.3 Multichannel Linear Systems Theory
332(2)
15.4 Multichannel Random Process Theory
334(3)
15.5 Multichannel Classical Spectral Estimators
337(3)
15.6 Multichannel ARMA, AR, and MA Processes
340(3)
15.7 Multichannel Yule-Walker Equations
343(2)
15.8 Multichannel Levinson Algorithm
345(3)
15.9 Multichannel Block Toeplitz Matrix Inverse
348(1)
15.10 Multichannel Autoregressive Spectral Estimation
349(7)
15.11 Autoregressive Order Selection
356(1)
15.12 Experimental Comparison of Multichannel Autogressive PSD Estimators
356(7)
15.13 Multichannel Minimum Variance Spectral Estimation
363(1)
15.14 Two-Channel Spectral Analysis of Sunspot Numbers and Air Temperature
364(3)
References
367(2)
16 Two-Dimensional Spectral Estimation 369(34)
16.1 Introduction
369(1)
16.2 Summary
370(2)
16.3 Two-Dimensional Linear Systems and Transform Theory
372(5)
16.4 Two-Dimensional Random Process Theory
377(3)
16.5 Classical 2-13 Spectral Estimation
380(3)
16.6 Modified Classical 2-D Spectral Estimators
383(1)
16.7 Two-Dimensional Autoregressive Spectral Estimation
384(13)
16.8 Two-Dimensional Maximum Entropy Spectral Estimation
397(1)
16.9 Two-Dimensional Minimum Variance Spectral Estimation
398(1)
References
399(4)
Index 403