Academic Press Library in Signal Processing, Volume 3

Introduction

Signal Processing at Your Fingertips!

About the Editors

Section Editors

Section 1

Section 2

Authors Biography

Section 1: Statistical Signal Processing

Chapter 1. Introduction to Statistical Signal Processing

Acknowledgments

3.01.1 A brief historical recount

3.01.2 Content

3.01.3 Contributions

3.01.4 Suggested further reading

References

Chapter 2. Model Order Selection

Abstract

3.02.1 Introduction

3.02.2 Example: variable selection in regression

3.02.3 Methods based on statistical inference paradigms

3.02.4 Information and coding theory based methods

3.02.5 Example: estimating number of signals in subspace methods

3.02.6 Conclusion

References

Chapter 3. Non-Stationary Signal Analysis Time-Frequency Approach

Abstract

3.03.1 Introduction

3.03.2 Linear signal transforms

3.03.3 Quadratic time-frequency distributions

3.03.4 Higher order time-frequency representations

3.03.5 Processing of sparse signals in time-frequency

3.03.6 Examples of time-frequency analysis applications

References

Chapter 4. Bayesian Computational Methods in Signal Processing

Abstract

3.04.1 Introduction

3.04.2 Parameter estimation

3.04.3 Computational methods

3.04.4 State-space models and sequential inference

3.04.5 Conclusion

A Probability densities and integrals

References

Chapter 5. Distributed Signal Detection

Abstract

3.05.1 Introduction

3.05.2 Distributed detection with independent observations

3.05.3 Distributed detection with dependent observations

3.05.4 Conclusion

References

Chapter 6. Quickest Change Detection

Abstract

Acknowledgments

3.06.1 Introduction

3.06.2 Mathematical preliminaries

3.06.3 Bayesian quickest change detection

3.06.4 Minimax quickest change detection

3.06.5 Relationship between the models

3.06.6 Variants and generalizations of the quickest change detection problem

3.06.7 Applications of quickest change detection

3.06.8 Conclusions and future directions

References

Chapter 7. Geolocation—Maps, Measurements, Models, and Methods

Abstract

Acknowledgment

3.07.1 Introduction

3.07.2 Theory—overview

3.07.3 Estimation methods

3.07.4 Motion models

3.07.5 Maps and applications

3.07.6 Mapping in practice

3.07.7 Conclusion

References

Chapter 8. Performance Analysis and Bounds

Abstract

3.08.1 Introduction

3.08.2 Parametric statistical models

3.08.3 Maximum likelihood estimation and the CRB

3.08.4 Mean-square error bound

3.08.5 Perturbation methods for algorithm analysis

3.08.6 Constrained Cramér-Rao bound and constrained MLE

3.08.7 Multiplicative and non-Gaussian noise

3.08.8 Asymptotic analysis and the central limit theorem

3.08.9 Asymptotic analysis and parametric models

3.08.10 Monte Carlo methods

3.08.11 Confidence intervals

3.08.12 Conclusion

References

Chapter 9. Diffusion Adaptation Over Networks

Abstract

Acknowledgments

3.09.1 Motivation

3.09.2 Mean-square-error estimation

3.09.3 Distributed optimization via diffusion strategies

3.09.4 Adaptive diffusion strategies

3.09.5 Performance of steepest-descent diffusion strategies

3.09.6 Performance of adaptive diffusion strategies

3.09.7 Comparing the performance of cooperative strategies

3.09.8 Selecting the combination weights

3.09.9 Diffusion with noisy information exchanges

3.09.10 Extensions and further considerations

Appendices

References

Section 2: Array Signal Processing

Chapter 10. Array Signal Processing: Overview of the Included Chapters

3.10.1 Some history

3.10.2 Summary of the included chapters

3.10.3 Outlook

References

Chapter 11. Introduction to Array Processing

Abstract

3.11.1 Introduction

3.11.2 Geometric data model

3.11.3 Spatial filtering and beam patterns

3.11.4 Beam forming and signal detection

3.11.5 Direction-of-arrival estimation

3.11.6 Non-coherent array applications

3.11.7 Concluding remarks

References

Chapter 12. Adaptive and Robust Beamforming

Abstract

Acknowledgments

3.12.1 Introduction

3.12.2 Data and beamforming models

3.12.3 Adaptive beamforming

3.12.4 Robust adaptive beamforming

References

Chapter 13. Broadband Beamforming and Optimization

Abstract

3.13.1 Introduction

3.13.2 Environment and channel modeling

3.13.3 Broadband beamformer design in element space

3.13.4 Broadband beamformer design using the wave equation

3.13.5 Optimum and adaptive broadband beamforming

3.13.6 Conclusion

References

Chapter 14. DOA Estimation Methods and Algorithms

Abstract

Acknowledgments

3.14.1 Background

3.14.2 Data model

3.14.3 Beamforming methods

3.14.4 Subspace methods

3.14.5 Parametric methods

3.14.6 Wideband DOA estimation

3.14.7 Signal detection

3.14.8 Special topics

3.14.9 Discussion

References

Chapter 15. Subspace Methods and Exploitation of Special Array Structures

Abstract

Acknowledgment

3.15.1 Introduction

3.15.2 Data model

3.15.3 Subspace estimation

3.15.4 Subspace-based algorithms

3.15.5 Conclusions

References

Chapter 16. Performance Bounds and Statistical Analysis of DOA Estimation

Abstract

3.16.1 Introduction

3.16.2 Models and basic assumption

3.16.3 General statistical tools for performance analysis of DOA estimation

3.16.4 Asymptotic distribution of estimated DOA

3.16.5 Detection of number of sources

3.16.6 Resolution of two closely spaced sources

References

Chapter 17. DOA Estimation of Nonstationary Signals

Abstract

3.17.1 Introduction

3.17.2 Nonstationary signals and time-frequency representations

3.17.3 Spatial time-frequency distribution

3.17.4 DOA estimation techniques

3.17.5 Joint DOD/DOA estimation in MIMO radar systems

3.17.6 Conclusion

References

Chapter 18. Source Localization and Tracking

Abstract

3.18.1 Introduction

3.18.2 Problem formulation

3.18.3 Triangulation

3.18.4 Signal propagation models

3.18.5 Source localization algorithms

3.18.6 Target tracking algorithm

3.18.7 Conclusion

References

Chapter 19. Array Processing in the Face of Nonidealities

Abstract

3.19.1 Introduction

3.19.2 Ideal array signal models

3.19.3 Examples of array nonidealities

3.19.4 Array calibration

3.19.5 Model-driven techniques

3.19.6 Data-driven techniques

3.19.7 Robust methods

3.19.8 Array processing examples

3.19.9 Conclusion

References

Chapter 20. Applications of Array Signal Processing

Abstract

3.20.1 Introduction and background

3.20.2 Radar applications

3.20.3 Radio astronomy

3.20.4 Positioning and navigation

3.20.5 Wireless communications

3.20.6 Biomedical

3.20.7 Sonar

3.20.8 Microphone arrays

3.20.9 Chemical sensor arrays

3.20.10 Conclusion

References and Further Reading

Index

This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing.

With this reference source you will:

• Quickly grasp a new area of research 
• Understand the underlying principles of a topic and its application
• Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved

• Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing
• Presents core principles and shows their application
• Reference content on core principles, technologies, algorithms and applications
• Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge
• Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

R&D engineers in signal processing and wireless and mobile communications