Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint
At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obs...
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| Published in | Sensors (Basel, Switzerland) Vol. 21; no. 13; p. 4390 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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26.06.2021
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| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s21134390 |
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| Abstract | At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios. |
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| AbstractList | At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios.At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios. At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios. |
| Author | Wang, Jian Zhang, Yalei Liu, Fei Zhang, Jian Cui, Ximin Yuan, Debao |
| AuthorAffiliation | 2 School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture (BUCEA), Beijing 102616, China; wangjian@bucea.edu.cn (J.W.); pntrc@cumt.edu.cn (F.L.); zhangyalei_hdxy@163.com (Y.Z.) 1 School of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing (CUMTB), Beijing 100083, China; yuandb@cumtb.edu.cn (D.Y.); cxm@cumtb.edu.cn (X.C.) |
| AuthorAffiliation_xml | – name: 2 School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture (BUCEA), Beijing 102616, China; wangjian@bucea.edu.cn (J.W.); pntrc@cumt.edu.cn (F.L.); zhangyalei_hdxy@163.com (Y.Z.) – name: 1 School of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing (CUMTB), Beijing 100083, China; yuandb@cumtb.edu.cn (D.Y.); cxm@cumtb.edu.cn (X.C.) |
| Author_xml | – sequence: 1 givenname: Debao surname: Yuan fullname: Yuan, Debao – sequence: 2 givenname: Jian surname: Zhang fullname: Zhang, Jian – sequence: 3 givenname: Jian surname: Wang fullname: Wang, Jian – sequence: 4 givenname: Ximin orcidid: 0000-0002-2736-9102 surname: Cui fullname: Cui, Ximin – sequence: 5 givenname: Fei orcidid: 0000-0001-6249-8674 surname: Liu fullname: Liu, Fei – sequence: 6 givenname: Yalei surname: Zhang fullname: Zhang, Yalei |
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| Cites_doi | 10.1109/SSP.2018.8450813 10.1109/IROS.2005.1545232 10.1109/JPROC.2008.2008846 10.1007/s00190-002-0256-7 10.1016/j.ejcon.2013.12.002 10.3390/mi6060747 10.1007/s11276-021-02631-0 10.3390/s19112578 10.1109/JSEN.2018.2789431 10.1109/IPSN.2008.15 10.1109/JSEN.2020.2981635 10.1007/s10015-005-0358-4 10.1109/JSEN.2019.2894714 10.1017/S0373463315001083 10.25103/jestr.071.02 10.1007/s11277-021-08425-z 10.1109/JSEN.2018.2873052 10.1007/s11277-007-9375-z 10.3390/ijgi5120224 10.1109/TWC.2014.2372341 10.1016/j.measurement.2008.10.002 10.1109/WCNC.2007.296 10.1179/003962608X325330 10.1109/JSEN.2009.2037013 10.1017/S0373463305003486 10.1016/j.jprocont.2013.12.017 10.1109/JSEN.2018.2818158 10.1109/TIM.2016.2608425 10.3390/s17092065 10.1109/MSP.2004.1359140 10.1088/1361-6501/ab87ea 10.1007/s10291-012-0254-z 10.1007/s001900000157 10.1016/j.ast.2010.08.007 10.1088/0957-0233/25/10/105011 10.3390/s150407096 10.3390/s20010193 10.1109/LCOMM.2020.3039251 10.1016/j.robot.2008.10.022 |
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| Title | Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint |
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