Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization
The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Inf...
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| Veröffentlicht in: | IEEE sensors journal 2021-03, Vol.21 (6), p.8479-8490 |
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| creator | Liu, Xu Zhou, Baoding Huang, Panpan Xue, Weixing Li, Qingquan Zhu, Jiasong Qiu, Li |
| description | The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method. |
| doi_str_mv | 10.1109/JSEN.2021.3050456 |
| format | Article |
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Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2021.3050456</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive filters ; Adaptive systems ; Algorithms ; Data analysis ; Data integration ; Dead reckoning ; Distance measurement ; Estimation ; Extended Kalman filter ; Federated filters ; fixed-interval filter ; indoor localization ; Inertial sensing devices ; Localization ; Localization method ; Location awareness ; Outliers (statistics) ; pedestrian dead reckoning (PDR) ; Robustness ; Sensors ; Smoothing methods ; Stability ; Time measurement ; Wi-Fi round trip time (RTT)/Wi-Fi fine time measurements (FTMs) ; Wireless fidelity</subject><ispartof>IEEE sensors journal, 2021-03, Vol.21 (6), p.8479-8490</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-ecfcc2622ddf378f8cf8fd84fee2d796b5651f3f467b0293e0f5e63eac2be1333</citedby><cites>FETCH-LOGICAL-c293t-ecfcc2622ddf378f8cf8fd84fee2d796b5651f3f467b0293e0f5e63eac2be1333</cites><orcidid>0000-0001-7049-4518 ; 0000-0003-4134-8313 ; 0000-0002-2438-6046 ; 0000-0003-4461-8884 ; 0000-0003-1607-2626</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9319190$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9319190$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, Xu</creatorcontrib><creatorcontrib>Zhou, Baoding</creatorcontrib><creatorcontrib>Huang, Panpan</creatorcontrib><creatorcontrib>Xue, Weixing</creatorcontrib><creatorcontrib>Li, Qingquan</creatorcontrib><creatorcontrib>Zhu, Jiasong</creatorcontrib><creatorcontrib>Qiu, Li</creatorcontrib><title>Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.</description><subject>Adaptive filters</subject><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Data analysis</subject><subject>Data integration</subject><subject>Dead reckoning</subject><subject>Distance measurement</subject><subject>Estimation</subject><subject>Extended Kalman filter</subject><subject>Federated filters</subject><subject>fixed-interval filter</subject><subject>indoor localization</subject><subject>Inertial sensing devices</subject><subject>Localization</subject><subject>Localization method</subject><subject>Location awareness</subject><subject>Outliers (statistics)</subject><subject>pedestrian dead reckoning (PDR)</subject><subject>Robustness</subject><subject>Sensors</subject><subject>Smoothing methods</subject><subject>Stability</subject><subject>Time measurement</subject><subject>Wi-Fi round trip time (RTT)/Wi-Fi fine time measurements (FTMs)</subject><subject>Wireless fidelity</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM9LwzAYhoMoOKd_gHgJeM7Mj6ZJjjpXnRaVOdFbyNIEOrpmJt1B_3pbNjy93-F53w8eAC4JnhCC1c3T--xlQjElE4Y5znh-BEaEc4mIyOTxcDOMMia-TsFZSmuMiRJcjED5bJqNaWFRN52L6M4kV8F70xlY7FIdWhg8_KxRUcPFcglNW8G3-wX0IcJ5W4U-ymBNU_-arofPwYk3TXIXhxyDj2K2nD6i8vVhPr0tkaWKdchZby3NKa0qz4T00nrpK5l552glVL7iOSee-SwXK9w3HPbc5cwZS1eOMMbG4Hq_u43he-dSp9dhF9v-paaZopLnmRQ9RfaUjSGl6Lzexnpj4o8mWA_S9CBND9L0QVrfudp3aufcP68YUURh9gfD12bt</recordid><startdate>20210315</startdate><enddate>20210315</enddate><creator>Liu, Xu</creator><creator>Zhou, Baoding</creator><creator>Huang, Panpan</creator><creator>Xue, Weixing</creator><creator>Li, Qingquan</creator><creator>Zhu, Jiasong</creator><creator>Qiu, Li</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. 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| subjects | Adaptive filters Adaptive systems Algorithms Data analysis Data integration Dead reckoning Distance measurement Estimation Extended Kalman filter Federated filters fixed-interval filter indoor localization Inertial sensing devices Localization Localization method Location awareness Outliers (statistics) pedestrian dead reckoning (PDR) Robustness Sensors Smoothing methods Stability Time measurement Wi-Fi round trip time (RTT)/Wi-Fi fine time measurements (FTMs) Wireless fidelity |
| title | Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization |
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