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

  • Yuan XuEmail author
  • Tao Shen
  • Xi-Yuan Chen
  • Li-Li Bu
  • Ning Feng
Research Article


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.


Indoor human localization tightly-coupled model predictive filtering Kalman filter missing data 


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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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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringUniversity of JinanJinanChina
  2. 2.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

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