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Networked Filtering and Fusion in Wireless Sensor Networks

Langue : Anglais

Auteurs :

Couverture de l’ouvrage Networked Filtering and Fusion in Wireless Sensor Networks

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.

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
     Chapter organization
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 
Wireless Sensor Network Security
     Obstacles of sensor security 
     Security requirements
Proposed Topics

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

Distributed Kalman Filtering
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
     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
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
Proposed Topics

Wireless Estimation Methods
Partitioned Kalman Filters
     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
Proposed Topics

Multi-Sensor Fault Estimation
     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
Proposed Topics

Multi-Sensor Data Fusion
     Multi-sensor data fusion
     Challenging problems 
     Multi–sensor data fusion approaches 
     Multi–sensor algorithms
Fault Monitoring 
     Problem Formulation
     Discrete time UKF
     Unscented procedure 
     Parameter estimation 
     Improved MSDF techniques
Proposed Topics

Approximate Distributed Estimation
Problem Formulation
Fusion with Complete Prior Information 
     Modified Kalman filter-I
     Lower-bound KF-I
     Upper-bound KF-I
     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
Proposed Topics

Estimation via Information Matrix
Problem Formulation
Covariance Intersection
Covariance Intersection Filter
     Complete feedback case 
     Partial feedback case
Weighted Covariance 
     Complete feedback case
     Partial feedback case
Kalman-Like Particle Filter 
     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
Proposed Topics

Filtering in Sensor Networks
Distributed H∞ Filtering
     System analysis 
     Simulation example 1
Distributed Cooperative Filtering 
     Problem formulation
     Centralized estimation 
     Distributed estimation 
     Issues of implementation
Distributed Consensus Filtering 
     Problem formulation
     Filter design: fully-equipped controllers 
     Filter design: pinning controllers 
     Simulation example 2
Distributed Fusion Filtering 
     Problem statement
     Two-stage distributed estimation 
     Distributed fusion algorithm 
     Simulation example 3
Distributed Filtering over Finite Horizon 
     Problem description
     Performance analysis 
     Distributed H∞ consensus filters design 
     Simulation example 4
Proposed Topics

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

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.
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