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Academic Press Library in Signal Processing

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

    1. Cover image
    2. Title page
    3. Table of Contents
    4. Copyright
    5. Introduction
      1. Signal Processing at Your Fingertips!
    6. About the Editors
    7. Section Editors
      1. Section 1
      2. Section 2
    8. Authors Biography
    9. Section 1: Statistical Signal Processing
      1. Chapter 1. Introduction to Statistical Signal Processing
        1. Acknowledgments
        2. 3.01.1 A brief historical recount
        3. 3.01.2 Content
        4. 3.01.3 Contributions
        5. 3.01.4 Suggested further reading
        6. References
      2. Chapter 2. Model Order Selection
        1. Abstract
        2. 3.02.1 Introduction
        3. 3.02.2 Example: variable selection in regression
        4. 3.02.3 Methods based on statistical inference paradigms
        5. 3.02.4 Information and coding theory based methods
        6. 3.02.5 Example: estimating number of signals in subspace methods
        7. 3.02.6 Conclusion
        8. References
      3. Chapter 3. Non-Stationary Signal Analysis Time-Frequency Approach
        1. Abstract
        2. 3.03.1 Introduction
        3. 3.03.2 Linear signal transforms
        4. 3.03.3 Quadratic time-frequency distributions
        5. 3.03.4 Higher order time-frequency representations
        6. 3.03.5 Processing of sparse signals in time-frequency
        7. 3.03.6 Examples of time-frequency analysis applications
        8. References
      4. Chapter 4. Bayesian Computational Methods in Signal Processing
        1. Abstract
        2. 3.04.1 Introduction
        3. 3.04.2 Parameter estimation
        4. 3.04.3 Computational methods
        5. 3.04.4 State-space models and sequential inference
        6. 3.04.5 Conclusion
        7. A Probability densities and integrals
        8. References
      5. Chapter 5. Distributed Signal Detection
        1. Abstract
        2. 3.05.1 Introduction
        3. 3.05.2 Distributed detection with independent observations
        4. 3.05.3 Distributed detection with dependent observations
        5. 3.05.4 Conclusion
        6. References
      6. Chapter 6. Quickest Change Detection
        1. Abstract
        2. Acknowledgments
        3. 3.06.1 Introduction
        4. 3.06.2 Mathematical preliminaries
        5. 3.06.3 Bayesian quickest change detection
        6. 3.06.4 Minimax quickest change detection
        7. 3.06.5 Relationship between the models
        8. 3.06.6 Variants and generalizations of the quickest change detection problem
        9. 3.06.7 Applications of quickest change detection
        10. 3.06.8 Conclusions and future directions
        11. References
      7. Chapter 7. Geolocation—Maps, Measurements, Models, and Methods
        1. Abstract
        2. Acknowledgment
        3. 3.07.1 Introduction
        4. 3.07.2 Theory—overview
        5. 3.07.3 Estimation methods
        6. 3.07.4 Motion models
        7. 3.07.5 Maps and applications
        8. 3.07.6 Mapping in practice
        9. 3.07.7 Conclusion
        10. References
      8. Chapter 8. Performance Analysis and Bounds
        1. Abstract
        2. 3.08.1 Introduction
        3. 3.08.2 Parametric statistical models
        4. 3.08.3 Maximum likelihood estimation and the CRB
        5. 3.08.4 Mean-square error bound
        6. 3.08.5 Perturbation methods for algorithm analysis
        7. 3.08.6 Constrained Cramér-Rao bound and constrained MLE
        8. 3.08.7 Multiplicative and non-Gaussian noise
        9. 3.08.8 Asymptotic analysis and the central limit theorem
        10. 3.08.9 Asymptotic analysis and parametric models
        11. 3.08.10 Monte Carlo methods
        12. 3.08.11 Confidence intervals
        13. 3.08.12 Conclusion
        14. References
      9. Chapter 9. Diffusion Adaptation Over Networks
        1. Abstract
        2. Acknowledgments
        3. 3.09.1 Motivation
        4. 3.09.2 Mean-square-error estimation
        5. 3.09.3 Distributed optimization via diffusion strategies
        6. 3.09.4 Adaptive diffusion strategies
        7. 3.09.5 Performance of steepest-descent diffusion strategies
        8. 3.09.6 Performance of adaptive diffusion strategies
        9. 3.09.7 Comparing the performance of cooperative strategies
        10. 3.09.8 Selecting the combination weights
        11. 3.09.9 Diffusion with noisy information exchanges
        12. 3.09.10 Extensions and further considerations
        13. Appendices
        14. References
    10. Section 2: Array Signal Processing
      1. Chapter 10. Array Signal Processing: Overview of the Included Chapters
        1. 3.10.1 Some history
        2. 3.10.2 Summary of the included chapters
        3. 3.10.3 Outlook
        4. References
      2. Chapter 11. Introduction to Array Processing
        1. Abstract
        2. 3.11.1 Introduction
        3. 3.11.2 Geometric data model
        4. 3.11.3 Spatial filtering and beam patterns
        5. 3.11.4 Beam forming and signal detection
        6. 3.11.5 Direction-of-arrival estimation
        7. 3.11.6 Non-coherent array applications
        8. 3.11.7 Concluding remarks
        9. References
      3. Chapter 12. Adaptive and Robust Beamforming
        1. Abstract
        2. Acknowledgments
        3. 3.12.1 Introduction
        4. 3.12.2 Data and beamforming models
        5. 3.12.3 Adaptive beamforming
        6. 3.12.4 Robust adaptive beamforming
        7. References
      4. Chapter 13. Broadband Beamforming and Optimization
        1. Abstract
        2. 3.13.1 Introduction
        3. 3.13.2 Environment and channel modeling
        4. 3.13.3 Broadband beamformer design in element space
        5. 3.13.4 Broadband beamformer design using the wave equation
        6. 3.13.5 Optimum and adaptive broadband beamforming
        7. 3.13.6 Conclusion
        8. References
      5. Chapter 14. DOA Estimation Methods and Algorithms
        1. Abstract
        2. Acknowledgments
        3. 3.14.1 Background
        4. 3.14.2 Data model
        5. 3.14.3 Beamforming methods
        6. 3.14.4 Subspace methods
        7. 3.14.5 Parametric methods
        8. 3.14.6 Wideband DOA estimation
        9. 3.14.7 Signal detection
        10. 3.14.8 Special topics
        11. 3.14.9 Discussion
        12. References
      6. Chapter 15. Subspace Methods and Exploitation of Special Array Structures
        1. Abstract
        2. Acknowledgment
        3. 3.15.1 Introduction
        4. 3.15.2 Data model
        5. 3.15.3 Subspace estimation
        6. 3.15.4 Subspace-based algorithms
        7. 3.15.5 Conclusions
        8. References
      7. Chapter 16. Performance Bounds and Statistical Analysis of DOA Estimation
        1. Abstract
        2. 3.16.1 Introduction
        3. 3.16.2 Models and basic assumption
        4. 3.16.3 General statistical tools for performance analysis of DOA estimation
        5. 3.16.4 Asymptotic distribution of estimated DOA
        6. 3.16.5 Detection of number of sources
        7. 3.16.6 Resolution of two closely spaced sources
        8. References
      8. Chapter 17. DOA Estimation of Nonstationary Signals
        1. Abstract
        2. 3.17.1 Introduction
        3. 3.17.2 Nonstationary signals and time-frequency representations
        4. 3.17.3 Spatial time-frequency distribution
        5. 3.17.4 DOA estimation techniques
        6. 3.17.5 Joint DOD/DOA estimation in MIMO radar systems
        7. 3.17.6 Conclusion
        8. References
      9. Chapter 18. Source Localization and Tracking
        1. Abstract
        2. 3.18.1 Introduction
        3. 3.18.2 Problem formulation
        4. 3.18.3 Triangulation
        5. 3.18.4 Signal propagation models
        6. 3.18.5 Source localization algorithms
        7. 3.18.6 Target tracking algorithm
        8. 3.18.7 Conclusion
        9. References
      10. Chapter 19. Array Processing in the Face of Nonidealities
        1. Abstract
        2. 3.19.1 Introduction
        3. 3.19.2 Ideal array signal models
        4. 3.19.3 Examples of array nonidealities
        5. 3.19.4 Array calibration
        6. 3.19.5 Model-driven techniques
        7. 3.19.6 Data-driven techniques
        8. 3.19.7 Robust methods
        9. 3.19.8 Array processing examples
        10. 3.19.9 Conclusion
        11. References
      11. Chapter 20. Applications of Array Signal Processing
        1. Abstract
        2. 3.20.1 Introduction and background
        3. 3.20.2 Radar applications
        4. 3.20.3 Radio astronomy
        5. 3.20.4 Positioning and navigation
        6. 3.20.5 Wireless communications
        7. 3.20.6 Biomedical
        8. 3.20.7 Sonar
        9. 3.20.8 Microphone arrays
        10. 3.20.9 Chemical sensor arrays
        11. 3.20.10 Conclusion
        12. References and Further Reading
    11. Index