Sequential fusion estimation for Markov jump systems with heavy-tailed noises

Abstract

We study a sequential fusion estimation problem for Markov jump multi-sensor systems with heavy-tailed noises. By modelling the noises as Student's t distributions, a sequential fusion estimation algorithm is designed by utilising the interacting multiple model method and Bayes' rule. To improve the robustness against measurement outliers caused by measurement heavy-tailed noise, an F-distribution detection strategy is designed to detect and reject the measurement outliers. Simulation results demonstrate that the designed sequential fusion estimation algorithm can effectively fuse the measurements from multiple sensors, and the accuracy of the designed algorithm is superior to the existing interacting multiple model Student's t batch fusion algorithm and single model adaptive Student's t batch fusion algorithm when there exist model switching and disturbances with heavy-tailed property.

Keywords:

• Sequential fusion
• Markov jump systems
• heavy-tailed noise
• sensor networks
• Student's t distribution

Disclosure statement

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

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant 62073036 and the Beijing Natural Science Foundation under Grant 4202071.

Notes on contributors

Hui Li

Hui Li received his B.S. degree in electrical engineering and automation from Hebei University of Science and Technology, Shijiazhuang, China, in 2016, and the M.S. degree in navigation guidance and control from Yanshan University, Qinhuangdao, China, in 2019. He is currently pursuing the Ph.D. degree in control science and engineering from the School of Automation, Beijing Institute of Technology, Beijing, China. His research interests include multisensor data fusion, intelligent navigation and integrated navigation.

Liping Yan

Liping Yan was born in Henan, China, in 1979. She received the B.S. and M.S. degrees in mathematics from Henan University, Kaifeng, China, in 2000 and 2003, respectively, and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2007. From 2007 to 2009, she was a postdoctoral research associate with the Equipment Academy of Air Force, Beijing. Since 2009, she has been with the School of Automation, Beijing Institute of Technology (BIT), Beijing, first as an assistant professor, from 2011 to 2021 as associate professor, then, since 2021, as full professor. From 2012 to 2013, supported by CSC, she was a visiting scholar with the University of New Orleans, New Orleans, LA, USA. From September 2018 to August 2019, she was a visiting scholar with the University of Windsor, Windsor, ON, Canada. She has coauthored five monographs and over 60 journal and conference papers. Her current research interests include multi-sensor data fusion, target tracking, fault detection, image registration, intelligent navigation and integrated navigation.

Yuqin Zhou

Yuqin Zhou received the B.S. and M.S. degrees from Beijing Technology and Business University, Beijing, China, in 2017 and 2020, respectively. She is currently pursuing the Ph.D. degree in control science and engineering from the School of Automation, Beijing Institute of Technology, Beijing, China. Her research interests include multi-target tracking, multi-sensor data fusion and risk assessment.

Yuanqing Xia

Yuanqing Xia was born in Anhui, China, in 1971. He received his Ph.D. degree in control theory and control engineering from Beihang University, Beijing, China, in 2001. He was a research fellow in several academic institutions during 2002 to 2008, including the National University of Singapore and the University of Glamorgan, UK. Since 2004, he has been with Beijing Institute of Technology (BIT), China, where he is currently a chair professor, as well as the chief director of the School of Automation, BIT. He is currently the director of specialised committee on cloud control and decision of Chinese Institute of Command and Control (CICC), a member of the 8th Disciplinary Review Group of the Academic Degrees Committee of the State Council, a member of the Big Data Expert Committee of the Chinese Computer Society and the vice chairman of the Internet of Things Working Committee of the Chinese Institute of Instrumentation. He was granted by the National Outstanding Youth Foundation of China in 2012 and was honoured as the Yangtze River Scholar Distinguished Professor in 2016 and the Leading Talent of the Chinese Ten Thousand Talents Program. He has published 16 monographs in Springer, John Wiley, and CRC, and more than 400 papers in international scientific journals, and has been a highly cited scholar since 2014 by Elsevier. He is a deputy editor of the Journal of Beijing Institute of Technology, an associate editor of Acta Automatica Sinica, International Journal of Automation and Computing, Gyroscopy and Navigation, Control Theory and Applications, etc. He obtained the Second Award of Beijing Municipal Science and Technology (No. 1) in 2010 and 2015, the Second National Award for Science and Technology (No. 2) in 2011, the Second Natural Science Award of the Ministry of Education (No. 1) in 2012 and 2017, and the Second Wu Wenjun Artificial Intelligence Award in 2018 (No. 1). More than five of his students have obtained the excellent doctoral thesis awards from Chinese Association of Automation or Chinese Institute of Command and Control. His research interests include cloud control systems, networked control systems, robust control and signal processing, active disturbance rejection control, unmanned system control and flight control.

Xiaodi Shi

Xiaodi Shi received the B.S. and M.S. degrees from Beijing Institute of Technology, Beijing, China, in 2017 and 2020, respectively. Her research interests include multisensor data fusion, intelligent navigation and integrated navigation.