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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.
Introduction
Optimal Filtering for Networked Systems with Channel Fading and Measurement Censoring
Tobit Kalman Filter with Time-Correlated Multiplicative Sensor Noise Under Redundant Channel Transmission
State Estimation Under Non-Gaussian L?vy and Time-Correlated Additive Sensor Noise
Protocol-Based Filter Design Under Integral Measurement and Probabilistic Sensor Failure
Distributed Filtering Fusion over Packet-Delaying Networks Subject to Censored Data
Federated Filtering Fusion with Dead-Zone-Like Censoring and Dynamical Bias Under Round-Robin Protocol
Multi-Sensor Filtering Fusion with Parametric Uncertainty and Measurement Censoring
Protocol-Based Filtering Fusion for State-Saturated Systems with Dead-Zone-Like Censoring Under Deception Attacks
Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems Under Stochastic Communication Protocol
Conclusion and Furture Topics