State estimation with multi-level vector quantisation and communication uncertainty

Abstract

In this paper, we design a state estimation algorithm for vector state-vector measurement systems over wireless sensor networks subject to bandwidth limitation and communication uncertainty. With the aid of Mahalanobis transformation, vector measurement innovations are decorrelated into the normalised ones to facilitate parallel quantisation. Then, taking account of Gaussian channel noises, a generalised multi-level quantisation mechanism and the minimum mean square error (MMSE) estimator are jointly designed, where optimal quantisation parameters can be solved by minimising the estimation error covariance with given quantisation level. The proposed MMSE estimator not only has a similar recursive structure as the classical Kalman filter, but also dramatically reduces the sensor-to-estimator communication requirement with only a slight deterioration of estimation performance. The combined effect of quantisation mechanism and communication uncertainty on estimation performance is also discussed. Finally, Monte Carlo simulation results illustrate the effectiveness and efficiency of the proposed quantised estimator.

Keywords:

• Quantised estimation
• multi-level quantisation
• communication uncertainty
• vector state-vector measurement

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61773055 and 62003275], and the Fundamental Research Funds for the Central Universities of China [grant number 31020190QD039], and Open Research Fund of Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems [grant number SKDN202005].

Notes on contributors

Zengwang Jin

Zengwang Jin received his Bachelor degree from School of Automation and Electrical Engineering, University of Science and Technology Beijing in 2013, where he is currently pursuing the Ph.D. degree in Control Science and Engineering. He was a visiting Ph.D. student with the Department of Mechanical, Industrial & Aerospace Engineering (MIAE), Concordia University, Montreal, QC, Canada, from October 2015 to October 2017. His research interest covers fault diagnosis, state estimation, stochastic hybrid systems and event-triggered systems.

Yanyan Hu

Yanyan Hu received her B.S. and M.S. degrees from School of the Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China, in 2003 and 2006, respectively. She received the Ph.D. degree from Department of Automation, Tsinghua University in 2011. She is an Associate Professor at School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her research interest currently focuses on fault diagnosis, stochastic hybrid systems, estimation and information fusion. The corresponding author of this paper.

Changyin Sun

Changyin Sun received his Bachelor degree with the College of Mathematics, Sichuan University, Chengdu, China, in 1996, and the M.S. and Ph.D. degrees in Electrical Engineering from Southeast University, Nanjing, China, in 2001 and 2003, respectively. He is a distinguished Professor with School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China and School of Automation, Southeast University, Nanjing, China. His current research interests include intelligent control, flight control, pattern recognition, and optimal theory. Prof. Sun is an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems.

Youmin Zhang

Youmin Zhang is currently a Professor with the Department of Mechanical, Industrial and Aerospace Engineering, and the Concordia Institute of Aerospace Design and Innovation, Concordia University, Canada. His main research interests include state estimation, fault detection and diagnosis, fault-tolerant control (FTC), fault-tolerant cooperative control of single and multiple unmanned aerial/space/ground/surface vehicles, smart grids, and the applications of unmanned systems to forest fires, power lines, environment, natural resources and disasters monitoring, detection, and protection by combining with remote sensing techniques. He has authored four books, over 500 journal and conference papers, and book chapters. He is a Fellow of the CSME, a Senior Member of AIAA, the Vice-President of the International Society of Intelligent Unmanned Systems, and a member of the Technical Committee for several scientific societies. He has served as the General Chair, the Program Chair, and an IPC Member of several international conferences. He has been an Editorial Board Member, the Editor-in-Chief, the Editor-at-Large, an Editor or an Associate Editor of several international journals.