{"title":"Indoor Positioning Using WiFi RSSI Trilateration and INS Sensor Fusion System Simulation","authors":"Abdelrahman El-Naggar, A. Wassal, K. Sharaf","doi":"10.1145/3365245.3365261","DOIUrl":null,"url":null,"abstract":"Indoor positioning is needed in many localization applications such as navigation, autonomous robotic movement, and asset tracking. In this paper, an indoor localization method based on fusion of WiFi RSSI positioning and inertial navigation system (INS) is proposed. Using WiFi positioning only is affected by the indoor communications environment that distort the RSSI signals, also using INS standalone solution has very degraded long-term performance, a Kalman filter (KF) is adopted in this paper to fuse and filter the RSSI signals with the INS data to have more accurate positioning results with average distance error of 0.6m.","PeriodicalId":151102,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Sensors, Signal and Image Processing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 2nd International Conference on Sensors, Signal and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3365245.3365261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Indoor positioning is needed in many localization applications such as navigation, autonomous robotic movement, and asset tracking. In this paper, an indoor localization method based on fusion of WiFi RSSI positioning and inertial navigation system (INS) is proposed. Using WiFi positioning only is affected by the indoor communications environment that distort the RSSI signals, also using INS standalone solution has very degraded long-term performance, a Kalman filter (KF) is adopted in this paper to fuse and filter the RSSI signals with the INS data to have more accurate positioning results with average distance error of 0.6m.
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