Book description
This third volume of a five volume set, 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
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Introduction
- About the Editors
- Section Editors
- Authors Biography
-
Section 1: Statistical Signal Processing
- Chapter 1. Introduction to Statistical Signal Processing
- Chapter 2. Model Order Selection
- Chapter 3. Non-Stationary Signal Analysis Time-Frequency Approach
- Chapter 4. Bayesian Computational Methods in Signal Processing
- Chapter 5. Distributed Signal Detection
-
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
-
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
- Chapter 11. Introduction to Array Processing
- Chapter 12. Adaptive and Robust Beamforming
- Chapter 13. Broadband Beamforming and Optimization
- Chapter 14. DOA Estimation Methods and Algorithms
- Chapter 15. Subspace Methods and Exploitation of Special Array Structures
- Chapter 16. Performance Bounds and Statistical Analysis of DOA Estimation
- Chapter 17. DOA Estimation of Nonstationary Signals
- Chapter 18. Source Localization and Tracking
- Chapter 19. Array Processing in the Face of Nonidealities
- Chapter 20. Applications of Array Signal Processing
- Index
Product information
- Title: Academic Press Library in Signal Processing
- Author(s):
- Release date: August 2013
- Publisher(s): Academic Press
- ISBN: 9780124116214
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