Academic Press Library in Signal Processing
Array and Statistical Signal Processing
- 1st Edition, Volume 3 - October 7, 2013
- Latest edition
- Editors: Mats Viberg, Sergios Theodoridis, Rama Chellappa, Abdelhak Zoubir
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 1 1 5 9 7 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 1 1 6 2 1 - 4
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 te… Read more
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
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
- Edition: 1
- Latest edition
- Volume: 3
- Published: October 7, 2013
- Language: English
ST
Sergios Theodoridis
Sergios Theodoridis is professor emeritus of machine learning and data processing with the National and Kapodistrian University of Athens, Athens, Greece. He has also served as distinguished professor with the Aalborg University Denmark and as professor with the Chinese University of Hong Kong, Shenzhen, China. In 2023, he received an honorary doctorate degree (D.Sc) from the University of Edinburgh, U.K. He has also received a number of prestigious awards, including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2017 European Association for Signal Processing (EURASIP) Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Carl Friedrich Gauss Education Award, and the 2014 EURASIP Meritorious Service Award. He has served as president of EURASIP and vice president for the IEEE Signal Processing Society. He is a Fellow of EURASIP and a Life Fellow of IEEE. He is the coauthor of the book Pattern Recognition, 4th edition, Academic Press, 2009 and of the book Introduction to Pattern Recognition: A MATLAB Approach, Academic Press, 2010.
Affiliations and expertise
Professor of Machine Learning and Signal Processing, National and Kapodistrian University of Athens, Athens, GreeceRC
Rama Chellappa
Prof. Rama Chellappa received the B.E. (Hons.) degree from the University of Madras, India, in 1975 and the M.E. (Distinction) degree from Indian Institute of Science, Bangalore, in 1977. He received M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of Electrical Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent Member). In 2005, he was named a Minta Martin Professor of Engineering. Prior to joining the University of Maryland, he was an Assistant (1981-1986) and Associate Professor (1986-1991) and Director of the Signal and Image Processing Institute (1988-1990) at University of Southern California, Los Angeles.
Over the last 29 years, he has published numerous book chapters, peer-reviewed journal and conference papers. He has co-authored and edited books on MRFs, face and gait recognition and collected works on image processing and analysis. His current research interests are face and gait analysis, markerless motion capture, 3D modeling from video, image and video-based recognition and exploitation and hyper spectral processing.
Affiliations and expertise
University of Maryland, College Park, USARead Academic Press Library in Signal Processing on ScienceDirect