Multi-Sensor Filtering Fusion with Censored Data under a Constrained Network Environment

Multi-Sensor Filtering Fusion with Censored Data under a Constrained Network Environment

This book presents the up-to-date research developments and novel methodologies on Multi-sensor filtering fusion (MSFF) for a class of complex systems subject to censored data under a constrained network environment.

Detalles Bibliográficos
Otros Autores: Geng, Hang, author (author), Wang, Zidong, 1966- author, Cheng, Yuhua, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2025]
Edición:First edition
Materias:
Sensor networks.
Multisensor data fusion.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869132006719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title
  • Copyright
  • Dedication
  • Contents
  • List of Figures
  • List of Tables
  • List of Symbols
  • Preface
  • Acknowledgement
  • Foreword
  • List of Contributors
  • Chapter 1 Introduction
  • 1.1 Canonical MSFF Schemes
  • 1.1.1 Centralized Filtering Fusion
  • 1.1.2 Information Filtering Fusion
  • 1.1.3 Sequential Filtering Fusion
  • 1.1.4 Weighted Filtering Fusion
  • 1.1.5 Covariance Intersection Fusion
  • 1.1.6 Federated Filtering Fusion
  • 1.2 Censored Measurements
  • 1.2.1 One-Side Censored Measurements
  • 1.2.2 Two-Side Censored Measurements
  • 1.2.3 Kalman Filtering with Censored Measurements
  • 1.3 Communication Constraints
  • 1.3.1 Communication Delays
  • 1.3.2 Fading Measurements
  • 1.3.3 Nonlinear Disturbances
  • 1.3.4 Quantized Measurements
  • 1.3.5 Disordered Measurements
  • 1.4 Outline
  • Chapter 2 Optimal Filtering for Networked Systems with Channel Fading and Measurement Censoring
  • 2.1 Problem Formulation
  • 2.2 Tobit Kalman Filter with Fading Measurements
  • 2.3 Illustrative Examples
  • 2.3.1 Oscillator Example
  • 2.3.2 Radar Tracking Example
  • 2.4 Summary
  • Chapter 3 Tobit Kalman Filter with Time-Correlated Multiplicative Sensor Noises under Redundant Channel Transmission
  • 3.1 Problem Formulation
  • 3.2 State-Dependent TKF under Redundant Channels
  • 3.3 An Illustrative Example
  • 3.4 Summary
  • Chapter 4 State Estimation under Non-Gaussian Lévy and Time-Correlated Additive Sensor Noises: A Modified Tobit Kalman Filtering Approach
  • 4.1 Problem Formulation
  • 4.2 A Modified Tobit Kalman Filter
  • 4.3 An Illustrative Example
  • 4.4 Summary
  • Chapter 5 Protocol-Based Filter Design under Integral Measurements and Probabilistic Sensor Failures: The Censored Data Case
  • 5.1 Problem Formulation
  • 5.2 Protocol-Based Tobit Kalman Filter
  • 5.3 Self-Propagating Lower and Upper Bounds.
  • 5.4 An Illustrative Example
  • 5.5 Summary
  • Chapter 6 Distributed Optimal Filtering Fusion over a Packet-Delaying Network Subject to Censored Data: A Probabilistic Perspective
  • 6.1 Problem Formulation
  • 6.2 Distributed Federated Tobit Kalman Filter with Packet Delays
  • 6.2.1 Local Tobit Kalman Filter with Packet Delays
  • 6.2.2 Distributed Tobit Kalman Filter with Packet Delays
  • 6.3 A Probabilistic Perspective
  • 6.4 An Illustrative Example
  • 6.5 Summary
  • Chapter 7 Federated Tobit Kalman Filtering Fusion with Dead-Zone-Like Censoring and Dynamical Bias under the Round-Robin Protocol
  • 7.1 Problem Formulation
  • 7.2 Main Results
  • 7.3 Illustrative Examples
  • 7.3.1 Oscillator Example
  • 7.3.2 Distributed Target Tracking Example
  • 7.4 Summary
  • Chapter 8 Multi-Sensor Filtering Fusion with Parametric Uncertainties and Measurement Censoring: Monotonicity and Boundedness
  • 8.1 Problem Formulation
  • 8.2 Design of the Fusion Estimator
  • 8.3 Boundedness and Monotonicity
  • 8.4 Illustrative Examples
  • 8.4.1 Oscillator Example
  • 8.4.2 Target Tracking Example
  • 8.5 Summary
  • Chapter 9 Protocol-Based Fusion Estimator Design for State-Saturated Systems with Dead-Zone-Like Censoring under Deception Attacks
  • 9.1 Problem Formulation
  • 9.2 Main Results
  • 9.3 An Illustrative Example
  • 9.4 Summary
  • Chapter 10 Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems with the Denial-of-Service Attacks and Stochastic Communication Protocol
  • 10.1 Problem Formulation
  • 10.2 Main Results
  • 10.3 An Illustrative Example
  • 10.4 Summary
  • Chapter 11 Conclusions and Future Topics
  • Bibliography
  • Index.

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