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Predictive Adaptive Kalman Filter and Its Application to INS/UWB-integrated Human Localization with Missing UWB-based Measurements

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Abstract

In order to improve the accuracy of the data fusion filter, a tightly-coupled ultra wide band (UWB)/inertial navigation system (INS)-integrated scheme for indoor human navigation will be investigated in this paper. In this scheme, the data fusion filter employs the difference between the INS-measured and UWB-measured distances as the observation. Moreover, the predictive adaptive Kalman filter (PAKF) for the tightly-coupled INS/UWB-integrated human tracking model with missing data of the UWB-measured distance will be designed, which considers the missing data of the UWB-based distance and employs the predictive UWB-measured distance. Real test results will be done to compare the performance of the Kalman filter (KF), adaptive Kalman filter (AKF), and the PAKF. The test results show that the performance of the AKF is better than the KF. Moreover, the proposed PAKF is able to maintain the performance of the filter when the UWB-based measurement is unavailable.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61803175 and 61773239), the China Postdoctoral Science Foundation (No. 2017 M622204), the Shandong Provincial Natural Science Foundation, China (Nos. ZR2018LF010 and ZR2015JL020), the High School Science and Technology Project in Shandong Province (No. J18KA333), and the Doctoral Foundation of the University of Jinan, China (No. XBS1503).

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Authors and Affiliations

  1. School of Electrical Engineering, University of Jinan, Jinan, 250022, China

    Yuan Xu, Tao Shen, Li-Li Bu & Ning Feng

  2. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Xi-Yuan Chen

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  1. Yuan Xu
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  2. Tao Shen
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  3. Xi-Yuan Chen
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Correspondence to Yuan Xu.

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Recommended by Associate Editor Yi Cao

Yuan Xu received the B. Sc. degree in automation from Shandong Polytechnic University, China in 2007, the M. Sc. degree in detection technology and automation device from Shandong Polytechnic University, China in 2010, and the Ph. D. degree in instrument science from Southeast University, China in 2014. He is currently a lecturer of School of Electrical Engineering with the University of Jinan, China. He is a member of IEEE. His research interests include integrated navigation and robust filtering.

Tao Shen received the Ph. D. degree in control theory and control engineering at Zhejiang University, China in 2004. Now he is the vice president of School of Electrical Engineering, University of Jinan, China. His research interests include stability of systems, intelligent control theory and method, robust control and production process control.

Xi-Yuan Chen received the B. Sc. degree in mechanical engineering from Lanzhou University of Technology, China in 1990, the M. Sc. degree in mechanical engineering from Hefei University of Technology, China in 1995, and the Ph. D. degree in precision instrument and mechanical engineering from Southeast University, China in 1998. He is currently the professor of School of Instrument Science and Engineering, Southeast university, China. He is a senior member of IEEE. His research interests include inertial navigation and integrated measurement.

Li-Li Bu received B. Sc. degree in automation from the University of Jinan, China in 2016, and now is a master student in control engineering at the University of Jinan, China. Her research interest is indoor pedestrian positioning.

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Xu, Y., Shen, T., Chen, XY. et al. Predictive Adaptive Kalman Filter and Its Application to INS/UWB-integrated Human Localization with Missing UWB-based Measurements. Int. J. Autom. Comput. 16, 604–613 (2019). https://doi.org/10.1007/s11633-018-1157-4

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