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Volume 6 Issue 4
Jul.  2019

IEEE/CAA Journal of Automatica Sinica

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Yuan Xu, Choon Ki Ahn, Yuriy S. Shmaliy, Xiyuan Chen and Lili Bu, "Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 952-960, July 2019. doi: 10.1109/JAS.2019.1911570
Citation: Yuan Xu, Choon Ki Ahn, Yuriy S. Shmaliy, Xiyuan Chen and Lili Bu, "Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 952-960, July 2019. doi: 10.1109/JAS.2019.1911570

Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering

doi: 10.1109/JAS.2019.1911570
Funds:  This work was supported in part by the National Natural Science Foundation of China (61803175), in part by the Project of Shandong Provincial Natural Science Foundation (ZR2018LF010)
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  • A combined algorithm for the loosely fused ultra wide band (UWB) and inertial navigation system (INS)-based measurements is designed under the indoor human navigation conditions with missing data. The scheme proposed fuses the INS- and UWB-derived positions via a data fusion filter. Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability, we also consider the missing data problem in UWB measurements. To overcome this problem, the loosely-coupled INS/UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response (UFIR) fusion filter. We show experimentally that, the standard UFIR fusion filter has higher robustness than the Kalman filter. It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.

     

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