Multi-Sensor Filtering Fusion with Censored Data Under a Constrained Network Environment [Hardcover]

1.Introduction 2. Optimal Filtering for Networked Systems with Channel Fading and Measurement Censoring 3. Tobit Kalman Filter with Time-Correlated Multiplicative Sensor Noise Under Redundant Channel Transmission 4. State Estimation Under Non-Gaussian L?vy and Time-Correlated Additive Sensor Noise 5. Protocol-Based Filter Design Under Integral Measurement and Probabilistic Sensor Failure 6. Distributed Filtering Fusion over Packet-Delaying Networks Subject to Censored Data 7. Federated Filtering Fusion with Dead-Zone-Like Censoring and Dynamical Bias Under Round-Robin Protocol 8. Multi-Sensor Filtering Fusion with Parametric Uncertainty and Measurement Censoring 9. Protocol-Based Filtering Fusion for State-Saturated Systems with Dead-Zone-Like Censoring Under Deception Attacks 10. Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems Under Stochastic Communication Protocol 11. Conclusion and Furture Topics

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.

Hang Geng received the B.Sc. degree in electronic information engineering in 2011 from Civil Aviation Flight University of China, the M.Sc. degree in electrical engineering and the Ph.D. degree in control science and engineering in 2014 and 2017, respectively, from Northwestern Polytechnical University. He is currently an Associate Professor at the School of Automation Engineering, University of Electronic Science and Technology of China. He was a visiting Ph.D. candidate (20152016) and a Research Fellow (20192022) with the Department of Computer Science, Brunel University London, UK. From 2018 to 2019, he was a Post- Doctoral Fellow with the Department of Mechanical Engineering, University of Kansas, USA. His research interests include information fusion, fault dialÞ