Indoor ins/UWB-based human localization with missing data utilizing predictive ufir filtering

Yuan Xu; Choon Ki Ahn; Yuriy S. Shmaliy; Xiyuan Chen; Lili Bu

doi:10.1109/JAS.2019.1911570

Indoor ins/UWB-based human localization with missing data utilizing predictive ufir filtering

Yuan Xu, Choon Ki Ahn, Yuriy S. Shmaliy, Xiyuan Chen, Lili Bu

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

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.

Original language	English
Article number	8753752
Pages (from-to)	952-960
Number of pages	9
Journal	IEEE/CAA Journal of Automatica Sinica
Volume	6
Issue number	4
DOIs	https://doi.org/10.1109/JAS.2019.1911570
Publication status	Published - 2019 Jul

Keywords

INS/UWB model
Indoor human navigation
Prediction
Unbiased fir filter

ASJC Scopus subject areas

Control and Systems Engineering
Information Systems
Artificial Intelligence

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title = "Indoor ins/UWB-based human localization with missing data utilizing predictive ufir filtering",

abstract = "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.",

keywords = "INS/UWB model, Indoor human navigation, Prediction, Unbiased fir filter",

author = "Yuan Xu and Ahn, {Choon Ki} and Shmaliy, {Yuriy S.} and Xiyuan Chen and Lili Bu",

note = "Funding Information: Manuscript received February 13, 2019; revised April 9, 2019; accepted May 14, 2019. 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). Recommended by Associate Editor Zhanshan Wang. (Corresponding author: Yuan Xu.) Citation: Y. Xu, C. K. Ahn, Y. S. Shmaliy, X. Y. Chen, and L. L. 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, Jul. 2019. Publisher Copyright: {\textcopyright} 2014 Chinese Association of Automation. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",

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doi = "10.1109/JAS.2019.1911570",

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N1 - Funding Information: Manuscript received February 13, 2019; revised April 9, 2019; accepted May 14, 2019. 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). Recommended by Associate Editor Zhanshan Wang. (Corresponding author: Yuan Xu.) Citation: Y. Xu, C. K. Ahn, Y. S. Shmaliy, X. Y. Chen, and L. L. 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, Jul. 2019. Publisher Copyright: © 2014 Chinese Association of Automation. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

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N2 - 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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Indoor ins/UWB-based human localization with missing data utilizing predictive ufir filtering

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Keywords

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