Networked Filtering and Fusion in Wireless Sensor Networks

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 H 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 H 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