Detection and Estimation Theory: and Its Applications (Paperback)

Description

For courses in Estimation and Detection Theory offered in departments of Electrical Engineering.

 

This is the first student-friendly textbook to comprehensively address the topics of both detection and estimation – with a thorough discussion of the underlying theory as well as the practical applications. By addressing detection and estimation theory in the same volume, the authors encourage a greater appreciation of the strong coupling and often blurring of these fields of study. In order to modernize classical topics, the text focuses on discrete signal processing with continuous signal presentations included to demonstrate uniformity and consistency of the results.

 

Part I Review Chapters

 

Chapter 1 Review of Probability

1.1 Chapter Highlights

1.2 Definition of Probability

1.3 Conditional Probability

1.4 Bayes’ Theorem

1.5 Independent Events

1.6 Random Variables

1.7 Conditional Distributions and Densities

1.8 Functions of One Random Variable

1.9 Moments of a Random Variable

1.10 Distributions with Two Random Variables

1.11 Multiple Random Variables

1.12 Mean-Square Error (MSE) Estimation

1.13 Bibliographical Notes

1.14 Problems

 

Chapter 2 Stochastic Processes

2.1 Chapter Highlights

2.2 Stationary Processes

2.3 Cyclostationary Processes

2.4 Averages and Ergodicity

2.5 Autocorrelation Function

2.6 Power Spectral Density

2.7 Discrete-Time Stochastic Processes

2.8 Spatial Stochastic Processes

2.9 Random Signals

2.10 Bibliographical Notes

2.11 Problems

 

Chapter 3 Signal Representations and Statistics

3.1 Chapter Highlights

3.2 Relationship of Power Spectral Density and Autocorrelation Function

3.3 Sampling Theorem

3.4 Linear Time-Invariant and Linear Shift-Invariant Systems

3.5 Bandpass Signal Representations

3.6 Bibliographical Notes

3.7 Problems

 

Part II Detection Chapters

 

Chapter 4 Single Sample Detection of Binary Hypotheses

4.1 Chapter Highlights

4.2 Hypothesis Testing and the MAP Criterion

4.3 Bayes Criterion

4.4 Minimax Criterion

4.5 Neyman-Pearson Criterion

4.6 Summary of Detection-Criterion Results Used in Chapter 4

Examples

4.7 Sequential Detection

4.8 Bibliographical Notes

4.9 Problems

 

Chapter 5 Multiple Sample Detection of Binary Hypotheses

5.1 Chapter Highlights

5.2 Examples of Multiple Measurements

5.3 Bayes Criterion

5.4 Other Criteria

5.5 The Optimum Digital Detector in Additive Gaussian Noise

5.6 Filtering Alternatives

5.7 Continuous Signals–White Gaussian Noise

5.8 Continuous Signals–Colored Gaussian Noise

5.9 Performance of Binary Receivers in AWGN

5.10 Further Receiver-Structure Considerations

5.11 Sequential Detection and Performance

5.12 Bibliographical Notes

5.13 Problems

 

Chapter 6 Detection of Signals with Random Parameters

6.1 Chapter Highlights

6.2 Composite Hypothesis Testing

6.3 Unknown Phase

6.4 Unknown Amplitude

6.5 Unknown Frequency

6.6 Unknown Time of Arrival

6.7 Bibliographical Notes

6.8 Problems

 

Chapter 7 Multiple Pulse Detection with Random Parameters

7.1 Chapter Highlights

7.2 Unknown Phase

7.3 Unknown Phase and Amplitude

7.4 Diversity Approaches and Performances

7.5 Unknown Phase, Amplitude, and Frequency

7.6 Bibliographical Notes

7.7 Problems

 

Chapter 8 Detection of Multiple Hypotheses

8.1 Chapter Highlights

8.2 Bayes Criterion

8.3 MAP Criterion

8.4 M-ary Detection Using Other Criteria

8.5 M-ary Decisions with Erasure

8.6 Signal-Space Representations

8.7 Performance of M-ary Detection Systems

8.8 Sequential Detection of Multiple Hypotheses

8.9 Bibliographical Notes

8.10 Problems

 

Chapter 9 Nonparametric Detection

9.1 Chapter Highlights

9.2 Sign Tests

9.3 Wilcoxon Tests

9.4 Other Nonparametric Tests

9.5 Bibliographical Notes

9.6 Problems

 

Part III Estimation Chapters

 

Chapter 10 Fundamentals of Estimation Theory

10.1 Chapter Highlights

10.2 Formulation of the General Parameter Estimation Problem

10.3 Relationship between Detection and Estimation Theory

10.4 Types of Estimation Problems

10.5 Properties of Estimators

10.6 Bayes Estimation

10.7 Minimax Estimation

10.8 Maximum-Likelihood Estimation

10.9 Comparison of Estimators of Parameters

10.10 Bibliographical Notes

10.11 Problems

 

Chapter 11 Estimation of Specific Parameters

11.1 Chapter Highlights

11.2 Parameter Estimation in White Gaussian Noise

11.3 Parameter Estimation in Nonwhite Gaussian Noise

11.4 Amplitude Estimation in the Coherent Case with WGN

11.5 Amplitude Estimation in the Noncoherent Case with WGN

11.6 Phase Estimation in WGN

11.7 Time-Delay Estimation in WGN

11.8 Frequency Estimation in WGN

11.9 Simultaneous Parameter Estimation in WGN

11.10 Whittle Approximation

11.11 Bibliographical Notes

11.12 Problems

 

Chapter 12 Estimation of Multiple Parameters

12.1 Chapter Highlights

12.2 ML Estimation for a Discrete Linear Observation Model

12.3 MAP Estimation for a Discrete Linear Observation Model

12.4 Sequential Parameter Estimation

12.5 Bibliographical References

12.6 Problems

 

Chapter 13 Distribution-Free Estimation–Wiener Filters

13.1 Chapter Highlights

13.2 Orthogonality Principle

13.3 Autoregressive Techniques

13.4 Discrete Wiener Filter

13.5 Continuous Wiener Filter

13.6 Generalization of Discrete and Continuous Filter Representations

13.7 Bibliographical Notes

13.8 Problems

 

Chapter 14 Distribution-Free Estimation–Kalman Filter

14.1 Chapter Highlights

14.2 Linear Least-Squares Methods

14.3 Minimum-Variance Weighted Least-Squares Methods

14.4 Minimum-Variance Least-Squares or Kalman Algorithm

14.5 Kalman Algorithm Computational Considerations

14.6 Kalman Algorithm for Signal Estimation

14.7 Continuous Kalman Filter

14.8 Extended Kalman Filter

14.9 Comments and Extensions

14.10 Bibliographical Notes

14.11 Problems

 

Part IV Application Chapters

 

Chapter 15 Detection and Estimation in Non-Gaussian Noise Systems

15.1 Chapter Highlights

15.2 Characterization of Impulsive Noise

15.3 Detector Structures in Non-Gaussian Noise

15.4 Selected Examples of Noise Models, Receiver Structures, and Error-Rate Performance

15.5 Estimation of Non-Gaussian Noise Parameters

15.6 Bibliographical Notes

15.7 Problems

 

Chapter 16 Direct-Sequence Spread-Spectrum Signals in Fading Multipath Channels

16.1 Chapter Highlights

16.2 Introduction to Direct-Sequence Spread Spectrum Communications

16.3 Fading Multipath Channel Models

16.4 Receiver Structures with Known Channel Parameters

16.5 Receiver Structures without Knowledge of Phase

16.6 Receiver Structures without Knowledge of Amplitude or Phase

16.7 Receiver Structures and Performance with No Channel Knowledge

16.8 Bibliographical Notes

16.9 Problems

 

Chapter 17 Multiuser Detection

17.1 Chapter Highlights

17.2 Introduction

17.3 Synchronous Multiuser Direct-Sequence CDMA

17.4 Asynchronous Multiuser Direct-Sequence CDMA

17.5 Speculative Summary

17.6 Bibliographical Notes

17.7 Problems

 

Chapter 18 Low-Probability-of-Intercept Communications

18.1 Chapter Highlights

18.2 LPI System Model

18.3 Interceptor Detector Structures

18.4 Filter-Bank Combiners

18.5 Feature Detectors

18.6 Bibliographical Notes

18.7 Problems

 

Chapter 19 Spectrum Estimation

19.1 Chapter Highlights

19.2 Overview of Power Spectral Estimation

19.3 Periodogram Techniques

19.4 Parametric Spectral Estimation Techniques

19.5 Examples of Spectral Estimation from MATLAB

19.6 Bibliographical Notes

19.7 Problems

 

Appendix A Properties of Distribution and Density Functions

 

Appendix B Common pdfs, cdfs, and Characteristic Functions

B.1 One Point

B.2 Zero-One

B.3 Binomial

B.4 Poisson

B.5 Uniform

B.6 Exponential

B.7 Gaussian-Based Distributions

B.8 Compilation of Mean, Variance, and Characteristic Function

 

Appendix C Multiple Normal Random Variables

C.1 Zero-Mean Jointly Normal Real Random Variables

C.2 Nonzero-Mean Jointly Normal Real Random Variables

C.3 Linear Transformation of Zero-Mean Jointly Normal Real Random

Variables

C.4 Central Limit Theorem 609

C.5 Nonzero Mean Jointly Normal Complex Random Variables

 

Appendix D Properties of Autocorrelation and Power Spectral Density Functions

D.1 Autocorrelation Functions–Continuous Processes

D.2 Power Spectral Density Functions–Continuous Process

D.3 Properties of Discrete Processes

 

Appendix E Equivalence of LTI and LSI Bandlimited Systems

 

Appendix F Theory of Random Sums

 

Appendix G Evaluations Useful for Chapters 6, 7, and 16

 

Appendix H Gram-Charlier Type Series

 

Appendix I Mobile User Detection

I.1 Overview of Commercial Cellular Networks

I.2 CDMA

I.3 Bibliographical Notes

 

Bibliography

Glossary

List of Symbols

Index