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Academic Press Library in Signal Processing - 1st Edition - ISBN: 9780124115972, 9780124116214

Academic Press Library in Signal Processing, Volume 3

1st Edition

Array and Statistical Signal Processing

Editors: Mats Viberg Abdelhak Zoubir
Editors in Chief: Sergios Theodoridis Rama Chellappa
Hardcover ISBN: 9780124115972
eBook ISBN: 9780124116214
Imprint: Academic Press
Published Date: 31st August 2013
Page Count: 1012
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Table of Contents


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


3.01.1 A brief historical recount

3.01.2 Content

3.01.3 Contributions

3.01.4 Suggested further reading


Chapter 2. Model Order Selection


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


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


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


Chapter 4. Bayesian Computational Methods in Signal Processing


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


Chapter 5. Distributed Signal Detection


3.05.1 Introduction

3.05.2 Distributed detection with independent observations

3.05.3 Distributed detection with dependent observations

3.05.4 Conclusion


Chapter 6. Quickest Change Detection



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


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



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


Chapter 8. Performance Analysis and Bounds


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


Chapter 9. Diffusion Adaptation Over Networks



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



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


Chapter 11. Introduction to Array Processing


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


Chapter 12. Adaptive and Robust Beamforming



3.12.1 Introduction

3.12.2 Data and beamforming models

3.12.3 Adaptive beamforming

3.12.4 Robust adaptive beamforming


Chapter 13. Broadband Beamforming and Optimization


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


Chapter 14. DOA Estimation Methods and Algorithms



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


Chapter 15. Subspace Methods and Exploitation of Special Array Structures



3.15.1 Introduction

3.15.2 Data model

3.15.3 Subspace estimation

3.15.4 Subspace-based algorithms

3.15.5 Conclusions


Chapter 16. Performance Bounds and Statistical Analysis of DOA Estimation


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


Chapter 17. DOA Estimation of Nonstationary Signals


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


Chapter 18. Source Localization and Tracking


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


Chapter 19. Array Processing in the Face of Nonidealities


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


Chapter 20. Applications of Array Signal Processing


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



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

Key Features

  • 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


No. of pages:
© Academic Press 2013
31st August 2013
Academic Press
Hardcover ISBN:
eBook ISBN:

Ratings and Reviews

About the Editors

Mats Viberg

Abdelhak Zoubir

About the Editors in Chief

Sergios Theodoridis

Sergios Theodoridis

Sergios Theodoridis is professor of machine learning and signal processing with the National and Kapodistrian University of Athens, Athens, Greece and with the Chinese University of Hong Kong, Shenzhen, China. He has 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 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 and as Editor-in-Chief IEEE Transactions on Signal processing. He is a Fellow of EURASIP and a Life Fellow of IEEE. He is the coauthor of the best selling 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

National and Kapodistrian University of Athens, Greece, and Chinese University of Hong Kong, Shenzhen, China.

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, USA