Multi-Sensor Filtering Fusion with Censored Data Under a Constrained Network

✍ Scribed by Hang Geng, Zidong Wang, Yuhua Cheng

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 263
• Edition: 1
• Category: Library

✦ Synopsis

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. The contents of this book are divided into two parts covering centralized and distributed MSFF design methodologies. The work provides a framework of optimal centralized/distributed filter design and stability and performance analysis for the considered systems along with designed filters. Simulations presented in this book are implemented using MATLAB.

Features:

• Includes concepts, backgrounds and models on censored data, filtering fusion and communication constraints.
• Reviews case studies to provide clear engineering insights into the developed fusion theories and techniques.
• Provides theoretic values and engineering insights of the censored data and constrained network.
• Discusses performance evaluation of the presented multi-sensor fusion algorithms.
• Explores promising research directions on future multi-sensor fusion.

This book is aimed at graduate students and researchers in networked control, sensor networks, and data fusion.

✦ Table of Contents

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