Distributed Kalman filtering for time-varying discrete sequential systems


Recent advances in the field of dynamical systems have led to the development of discrete sequential systems with applications in numerous areas including automation processes, digital computer mechanization, and series systems. Currently, designed centralized state estimation methods are used to monitor the operation of discrete sequential systems. This, however, depends on the measured state of the available individual local subsystems. Unfortunately, this approach involves high discrete sequential systems dimensions and collecting measurements form each and every local subsystem which is differently located so as to estimate their states.

This leads to a complex system that is generally time-consuming and expensive in terms of costs. As such, researchers have been looking for alternatives to address this challenge and have identified distributed state estimation approach as a promising solution. On the other hand, state estimation of the discrete sequential systems has not been fully explored.

To this note, Dr. Bo Chen, Dr. Guoqiang Hu at Nanyang Technological University in collaboration with Professor Daniel Ho at the City University of Hong Kong and Professor Li Yu from the Zhejiang University of Technology investigated distributed state estimation for time-varying discrete sequential systems. Fundamentally, they purposed to effectively enabled decoupling of large-scale interconnected systems so as to enhance their efficiency and applications. This included improving state estimation accuracy for the individual subsystems. Their research work is currently published in the research journal, Automatica.

In brief, the research team initiated their research work by cross-examining time-varying discrete sequential systems taking into consideration the Gaussian white noises. Next, they designed a local optimal distributed Kalman filter in minimum linear variance sense. In addition, with a bounded mean square error of the distributed estimator, a stable condition was effectively derived. This was performed while taking into consideration the distributed Kalman filter structure and the noise statistical properties. Furthermore, the possibility of using only one sensor to monitor an individual subsystem in the discrete sequential systems under distributed structure was also examined. So as to validate the effectiveness of the proposed method, the authors developed an illustrative method. This was realized by bounding the distributed Kalman filter mean square error with time-varying gains to infinity.

On the other hand, it was necessary to employ a target tracking system so as to demonstrate the feasibility of the system. From the experimental results, the authors observed that the estimation performance of the accuracy could be enhanced by developing certain fusion criteria. This however required design of multiple sensors fusion method for monitoring the operations of all the subsystems forming a discrete sequential system.

In summary, the research team successfully investigated state estimation problems in time-varying discrete sequential systems. From the demonstrated example, the authors noted that rather than using one sensor to monitor an individual subsystem, a multisensory fusion can significantly improve the estimation performance of the system. In general, the study provides vital information that will further advance future distributed Kalman filter based research work and especially in cases where multiple sensors are used to monitor individual subsystems.

Distributed Kalman filtering for time-varying discrete sequential systems - Advances in Engineering

About the author

Bo Chen received the B.S. degree in information and computing science from Jiangxi University of Science and Technology, Ganzhou, China, in 2008, and the Ph.D degree in Control Theory and Control Engineering from Zhejiang University of Technology, Hangzhou, China, in 2014. He is currently a tenure-tracked professor with the Institute of Cyberspace Security, Zhejiang University of Technology, China. He was a Research Fellow with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, from 2014 to 2015 and from 2017 to 2018. He was also a Post-Doctoral Research Fellow with the Department of Mathematics, City University of Hong Kong, Hong Kong, China, from 2015 to 2017.

He was a recipient of the outstanding thesis award of Chinese Association of Automation (CAA) in 2015. He serves as Editor-in-Chief for Information Fusion, Control and Decision. His current research interests include information fusion, distributed estimation and control, networked fusion systems and cyber-physical systems.

About the author

Guoqiang Hu joined the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore in 2011, and is currently a tenured Associate Professor and the Director of the Centre for System Intelligence and Efficiency (EXQUISITUS). He was an Assistant Professor at Kansas State University, Manhattan KS, USA, from 2008 to 2011. He received the B.Eng. degree in Automation from the University of Science and Technology of China in 2002, the M.Phil. degree in Automation and Computer-Aided Engineering from the Chinese University of Hong Kong in 2004, and the Ph.D. degree in Mechanical Engineering from the University of Florida in 2007.

His research focuses on analysis, control, design and optimization of distributed intelligent systems. More specifically, he works on distributed control, optimization and games, with applications to multi-robot systems and smart city systems. He was a recipient of the Best Paper in Automation Award in the 14th IEEE International Conference on Information and Automation in 2017, a recipient of the Best Paper Award (Guan Zhao-Zhi Award) in the 36th Chinese Control Conference in 2017, and a recipient of the Early Career Teaching Excellence Award at Nanyang Technological University, Singapore, in 2015.

He serves or served as Associate Editor for IEEE Transactions on Control Systems Technology, Technical Editor for IEEE/ASME Transactions on Mechatronics, Associate Editor for IEEE Transactions on Automation Science and Engineering, and Subject Editor for International Journal of Robust and Nonlinear Control.

About the author

Daniel W. C. Ho received the B.S., M.S., and Ph.D. degrees in mathematics from the University of Salford, Greater Manchester, U.K., in 1980, 1982, and 1986, respectively. From 1985 to 1988, he was a Research Fellow with the Industrial Control Unit, University of Strathclyde, Glasgow, U.K. In 1989, he joined the City University of Hong Kong, Hong Kong, where he is currently a Chair Professor of applied mathematics, and an Associate Dean with the College of Science.

He has over 230 publications in scientific journals. His current research interests include control and estimation theory, complex dynamical distributed networks, multiagent networks, and stochastic systems.

Prof. Ho is a Fellow of the IEEE. He was a recipient of the Chang Jiang Chair Professor Awarded by the Ministry of Education, China, in 2012 and the ISI Highly Cited Researchers Award in Engineering by Clarivate Analytics, from 2014 to 2018. He has been on the Editorial Board of a number of journals including the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IET Control Theory and Its Applications, the Journal of the Franklin Institute, and the Asian Journal of Control.

About the author

Li Yu received the B.S. degree in control theory from Nankai University, Tianjin, China, in 1982, and the M.S. and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1998 and 1999, respectively. He is currently a Professor in College of Information Engineering, Zhejiang University of Technology. He has authored or co-authored three books and over 200 journal papers. His current research interests include cyber–physical systems security, networked control systems, motion control and information fusion.


Chen, B., Hu, G., Ho, D., & Yu, L. (2019). Distributed Kalman filtering for time-varying discrete sequential systemsAutomatica99, 228-236.

Go To Automatica

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