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Variational Bayesian Adaptive Unscented Kalman Filter for RSSI-based Indoor Localization

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  • Control Theory and Applications
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Abstract

Most existing localization schemes necessitate a priori statistical characteristic of measurement noise, which may be unrealistic in practical applications. This paper investigates the variational Bayesian adaptive unscented Kalman filtering (VBAUKF) for received signal strength indication (RSSI) based indoor localization under inaccurate process and measurement noise covariance matrices. First, an inaccurate and slowly varying measurement noise covariance matrix can be estimated by choosing appropriate conjugate prior distribution for an indoor localization model with inaccurate process and measurement noise covariance matrices. By choosing inverse Wishart priors distribution, the state, predicted error and measurement noise covariance matrices are inferred on each time separately. Second, a parameter optimization algorithm is designed to minimize the localization error of VBAUKF until it less than the threshold set in advance. Finally, experimental validation is presented to demonstrate the accuracy and effectiveness of the proposed filtering method for indoor localization.

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Shanxi University, No.92 Wucheng Rd., Taiyuan, 030006, China

    Bo Yang

  2. Department of Automation, Shanxi University, No.92 Wucheng Rd, Taiyuan, Shanxi, 030006, China

    Xinchun Jia

  3. School of Engineering and Built Environment, Griffith University, Parklands Dr., Southport Gold Coast, QLD, 4222, Australia

    Fuwen Yang

Authors
  1. Bo Yang
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  2. Xinchun Jia
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  3. Fuwen Yang
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Corresponding author

Correspondence to Fuwen Yang.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Editor Hamid Reza Karimi. This work was supported by National Natural Science Foundation of China (Grant Nos. 61973201, U1610116). Natural Science Foundation of Shanxi under Grant No. 201801D221171. Key Research and Development Program of Shanxi (Grant No. 201903D121145).

Bo Yang received his M.Sc. degree in the College of Science and a Ph.D. degree in Institute of Electrical Engineering from Yanshan University, Qinhuangdao, China, in 2009 and 2013, respectively. He is currently a Lecturer with the School of Mathematics Sciences, Shanxi University, Taiyuan, China. From 2018 to 2019, he was a Visiting Scholar with the Griffith School of Engineering, Griffith University, Gold Coast, QLD, Australia. His current research interests include distributed filter, indoor localization and visual servoing.

Xinchun Jia received his B.S. degree from School of Automation and Software Engineering, Shanxi University, Taiyuan, China, an M.S. degree from the Institute of Systems Science, Chinese Academy of Sciences, Beijing, China, and a Ph.D. degree in control science and engineering from Xi’an Jiaotong University, Xi’an, China, in 1985, 1988, and 2003, respectively. He is currently a Professor and the Dean with the Department of Automation, Shanxi University. His current research interests include networked control systems, fuzzy control, multiagent systems, and intelligent control.

Fuwen Yang received a Ph.D. degrees in control engineering from Huazhong University of Science and Technology, China, in 1990. He is currently an Associate Professor at Griffith University, Australia. Before joining Griffith, he was a Research Fellow at Brunel University and King’s College London, UK, a Professor at Fuzhou University and East China University of Science and Technology, China, and an Associate Professor at Central Queensland University, Australia. He also held a Visiting Professor at the University of Manchester, UK, and the University of Hong Kong, Hong Kong. His current research interests include networked control systems, distributed filtering and sensing, reliable fault detection and diagnosis, distributed control and filtering, microgrids with renewable energy integration, and robot imitation learning for healthcare. Dr. Yang was a recipient of the Teaching Excellence Award for Young Teachers in 1995 from Fok Ying Tung Education Foundation, China; five Science and Technology Development Awards in 1996, 1999, 2002, 2006 and 2010. He is an Associate Editor of IEEE Industrial Electronics Magazine, Frontiers in Energy Research-Smart Grids, the Journal of the Franklin Institute, and the IEEE CSS Conference Editorial Board.

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Yang, B., Jia, X. & Yang, F. Variational Bayesian Adaptive Unscented Kalman Filter for RSSI-based Indoor Localization. Int. J. Control Autom. Syst. 19, 1183–1193 (2021). https://doi.org/10.1007/s12555-019-0973-9

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  • DOI: https://doi.org/10.1007/s12555-019-0973-9

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