Mahmoud / Xia Networked Filtering and Fusion in Wireless Sensor Networks

1. Auflage 2014
ISBN: 978-1-4822-5097-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 576 Seiten

By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and Fusion in Wireless Sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed systems.

The book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies.

Complete with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for WSNs.

The book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in practice.

After reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

Zielgruppe

Professionals and engineers who are designers or planners for sensor networks; researchers and graduate students who major in the field of computer science and electrical engineering; professionals and engineers who are designers or planners for sensor networks.

Autoren/Hrsg.

Mahmoud, Magdi S.

Xia, Yuanqing

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung

Inhaltsverzeichnis

Introduction
Overview

Fundamental Terms
Some Limitations
Information Fusion in Wireless Sensor Network
Classifying Information Fusion Classification based on relationship among the sources Classification based on levels of abstraction Classification based on input and output
Outline of the Book Methodology Chapter organization

Notes

Proposed Topics

Wireless Sensor Networks
Some Definitions
Common Characteristics

Required Mechanisms

Related Ingredients Key issues Types of sensor networks Main advantages

Sensor Networks Applications Military applications Environmental applications Health applications Application trends Hardware constraints
Routing Protocols System architecture and design issues Flooding and gossiping Sensor protocols for information via negotiation Directed diffusion Geographic and energy-aware routing Gradient-based routing Constrained anisotropic diffusion routing Active query forwarding Low-energy adaptive clustering hierarchy Power-efficient gathering Adaptive threshold sensitive energy efficient network Minimum energy communication network Geographic adaptive fidelity

Sensor Selection Schemes Sensor selection problem Coverage schemes Target tracking and localization schemes Single mission assignment schemes Multiple mission assignment schemes

Quality of Service Management QoS requirements Challenges

Wireless Sensor Network Security Obstacles of sensor security Security requirements

Notes
Proposed Topics

Distributed Sensor Fusion

Assessment of Distributed State Estimation Introduction Consensus-based distributed Kalman filter Simulation example 1
Distributed Sensor Fusion Introduction Consensus problems in networked systems Consensus filters Simulation example 2 Simulation example 3 Some observations

Estimation for Adaptive Sensor Selection Introduction Distributed estimation in dynamic systems Convergence properties Sensor selection for target tracking Selection of best active set Global node selection Spatial split Computational complexity Number of active sensors Simulation results
Multi-Sensor Management Primary purpose Role in information fusion Architecture classes Hybrid and hierarchical architectures Classification of related problems

Notes
Proposed Topics

Distributed Kalman Filtering
Introduction
Distributed Kalman Filtering Methods Different methods Pattern of applications Diffusion-based filtering Multi-sensor data fusion systems Distributed particle filtering Self-tuning based filtering
Information Flow Micro-Kalman filters Frequency-type consensus filters Simulation example 1 Simulation example 2
Consensus Algorithms in Sensor Networked Systems Basics of graph theory Consensus algorithms Simulation example 3 Simulation example 4
Application of Kalman Filter Estimation Preliminaries 802.11 distributed coordination function

Estimating the Competing Stations ARMA filter estimation Extended Kalman filter estimation Discrete state model Extended Kalman filter Selection of state noise statistics Change detection Performance evaluation

Notes

Proposed Topics

Expectation Maximization

General Considerations

Data-Fusion Fault Diagnostics Scheme Modeling with sensor and actuator faults Actuator faults Sensor faults The Expected maximization algorithm Initial system estimation Computing the input moments

Fault Isolation System description Fault model for rotational hydraulic drive Fault scenarios

EM Algorithm Implementation Leakage fault Controller fault
Notes
Proposed Topics

Wireless Estimation Methods
Partitioned Kalman Filters Introduction Centralized Kalman filter Parallel information filter Decentralized information filter Hierarchical Kalman filter Distributed Kalman filter with weighted averaging Distributed consensus Kalman filter Distributed Kalman filter with bipartite fusion graphs Simulation example A

Wireless Networked Control System Sources of wireless communication errors Structure of the WNCS Networked control design Simulation example B

Notes
Proposed Topics

Multi-Sensor Fault Estimation

Introduction Model-based schemes Model-free schemes Probabilistic schemes
Problem Statement
Improved Multi-Sensor Data Fusion Technique Unscented Kalman filter Unscented transformation Multi-sensor integration architectures Centralized integration method Decentralized integration method

Simulation Results An interconnected-tank process model Utility boiler
Notes
Proposed Topics

Multi-Sensor Data Fusion

Overview Multi-sensor data fusion Challenging problems Multi–sensor data fusion approaches Multi–sensor algorithms

Fault Monitoring Introduction Problem Formulation Discrete time UKF Unscented procedure Parameter estimation Improved MSDF techniques

Notes
Proposed Topics

Approximate Distributed Estimation
Introduction
Problem Formulation

Fusion with Complete Prior Information Modified Kalman filter-I Lower-bound KF-I Upper-bound KF-I Convergence Fusion without Prior Information Modified Kalman filter-II Upper-bound KF-II
Fusion with Incomplete Prior Information Modified Kalman filter-III Approximating the Kalman filter Lower-bound KF-III Upper-bound KF-III

Fusion Algorithm Evaluation and Testing Simulation results Time computation

Notes

Proposed Topics

Estimation via Information Matrix
Introduction
Problem Formulation

Covariance Intersection

Covariance Intersection Filter Algorithm Complete feedback case Partial feedback case

Weighted Covariance Algorithm Complete feedback case Partial feedback case
Kalman-Like Particle Filter Algorithm Complete feedback case Partial feedback case
Measurement Fusion Algorithm
Equivalence of Two Measurement Fusion Methods

Tracking Level Cases Illustrative example 1 Illustrative example 2
Testing and Evaluation Fault model for utility boiler Covariance intersection filter Weighted covariance filter Kalman-like particle filter Mean square error comparison

Notes
Proposed Topics

Filtering in Sensor Networks
Distributed H8 Filtering Introduction System analysis Simulation example 1

Distributed Cooperative Filtering Introduction Problem formulation Centralized estimation Distributed estimation Issues of implementation

Distributed Consensus Filtering Introduction Problem formulation Filter design: fully-equipped controllers Filter design: pinning controllers Simulation example 2

Distributed Fusion Filtering Introduction Problem statement Two-stage distributed estimation Distributed fusion algorithm Simulation example 3

Distributed Filtering over Finite Horizon Introduction Problem description Performance analysis Distributed H8 consensus filters design Simulation example 4

Notes
Proposed Topics

Appendix

A Glossary of Terminology and Notations General Terms Functional Differential Equations

Stability Notions Practical stabilizability Razumikhin stability

Delay Patterns

Lyapunov Stability Theorems Lyapunov-Razumikhin theorem Lyapunov-Krasovskii theorem Some Lyapunov-Krasovskii functionals

Algebraic Graph Theory Basic results Laplacian spectrum of graphs Properties of adjacency matrix

Minimum Mean Square Estimate

Gronwall–Bellman Inequalities

Basic Inequalities Inequality 1 Inequality 2 Inequality 3 Inequality 4 (Schur Complements) Inequality 5 Inequality 6 Bounding lemmas

Linear Matrix Inequalities Basics Some Standard Problems S-Procedure

Some Formulas on Matrix Inverses Inverse of Block Matrices Matrix inversion lemma Irreducible matrices

Über Autor(innen)

Magdi Sadek Mahmoud obtained BSc (Honors) in communication engineering, MSc in electronic engineering, and PhD in systems engineering, all from Cairo University in 1968, 1972, and 1974, respectively. He has been a professor of engineering since 1984. He is now a Distinguished University Professor at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He was on the faculty at different universities worldwide including Egypt (CU, AUC), Kuwait (KU), UAE (UAEU), UK (UMIST), USA (Pitt, Case Western), Singapore (Nanyang Technological) and Australia (Adelaide). He lectured in Venezuela (Caracas), Germany (Hanover), UK (Kent), USA (University of Texas at SA), Canada (Montreal, Alberta) and China (BIT, Yanshan). He is the principal author of thirty-four (34) books, inclusive book-chapters and the author/co-author of more than 510 peer-reviewed papers. He is the recipient of two national, one regional, and four university prizes for outstanding research in engineering and applied mathematics. He is a fellow of the IEE, a senior member of the IEEE, the CEI (UK), and a registered consultant engineer of information engineering and systems (Egypt). He is currently actively engaged in teaching and research in the development of modern methodologies of distributed control and filtering, networked-control systems, triggering mechanisms in dynamical systems, faulttolerant systems and information technology. He is a fellow of the IEE, a senior member of the IEEE, the CEI (UK), and a registered consultant engineer of information engineering and systems Egypt.

Yuanqing Xia was born in Anhui Province, China, in 1971 and graduated from the Department of Mathematics, Chuzhou University, Chuzhou, China, in 1991. He received his MS degree in Fundamental Mathematics from Anhui University, China, in 1998 and his PhD degree in Control Theory and Control Engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2001. From 1991-1995, he was with Tongcheng Middle-School as a teacher, Anhui, China. During January 2002 to November 2003, he was a postdoctoral research associate at the Institute of Systems Science, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing, China, where he worked on navigation, guidance and control. From November 2003 to February 2004, he joined the National University of Singapore as a research fellow, where he worked on variable structure control. From February 2004 to February 2006, he was with the University of Glamorgan, Pontypridd, U.K., as a research fellow, where he worked on networked control systems. From February 2007 to June 2008, he was a guest professor with Innsbruck Medical University, Innsbruck, Austria, where he worked on biomedical signal processing. Since July 2004, he has been with the Department of Automatic Control, Beijing Institute of Technology, Beijing, first as an associate professor, and then, since 2008, as a professor. His current research interests are in the fields of networked control systems, robust control, sliding mode control, active disturbance rejection control and biomedical signal processing.