{"title":"未知阴影和非视线对可见光室内跟踪的影响","authors":"Zafer Vatansever, M. Brandt-Pearce","doi":"10.1109/MILCOM.2017.8170836","DOIUrl":null,"url":null,"abstract":"Indoor target tracking has garnered interest as communication systems and mobile device capabilities advance. Visible light communication (VLC) is an alternative to RF methods that uses light emitting diodes. In this paper, probabilistic filtering algorithms (particle and extended Kalman filters) are used for indoor tracking. The performance of the filters is compared with an another positioning method: trilateration. Probabilistic filtering methods are shown to be more reliable than trilateration for indoor tracking when non-line-of-sight components are significant. The probabilistic filtering algorithms require a light intensity map that is collected as a fingerprint map prior to tracking. The effect of unpredictable shadowing as the conditions change is examined in a scenario where one of the lamps has been shadowed. Our algorithm tracks the user equipment with less error than trilateration. The results also show that the tracking accuracy for our algorithms is on the order of the grid resolution of the fingerprint map for high signal-to-noise ratio environments.","PeriodicalId":113767,"journal":{"name":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Effects of unknown shadowing and non-line-of-sight on indoor tracking using visible light\",\"authors\":\"Zafer Vatansever, M. Brandt-Pearce\",\"doi\":\"10.1109/MILCOM.2017.8170836\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indoor target tracking has garnered interest as communication systems and mobile device capabilities advance. Visible light communication (VLC) is an alternative to RF methods that uses light emitting diodes. In this paper, probabilistic filtering algorithms (particle and extended Kalman filters) are used for indoor tracking. The performance of the filters is compared with an another positioning method: trilateration. Probabilistic filtering methods are shown to be more reliable than trilateration for indoor tracking when non-line-of-sight components are significant. The probabilistic filtering algorithms require a light intensity map that is collected as a fingerprint map prior to tracking. The effect of unpredictable shadowing as the conditions change is examined in a scenario where one of the lamps has been shadowed. Our algorithm tracks the user equipment with less error than trilateration. The results also show that the tracking accuracy for our algorithms is on the order of the grid resolution of the fingerprint map for high signal-to-noise ratio environments.\",\"PeriodicalId\":113767,\"journal\":{\"name\":\"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MILCOM.2017.8170836\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM.2017.8170836","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effects of unknown shadowing and non-line-of-sight on indoor tracking using visible light
Indoor target tracking has garnered interest as communication systems and mobile device capabilities advance. Visible light communication (VLC) is an alternative to RF methods that uses light emitting diodes. In this paper, probabilistic filtering algorithms (particle and extended Kalman filters) are used for indoor tracking. The performance of the filters is compared with an another positioning method: trilateration. Probabilistic filtering methods are shown to be more reliable than trilateration for indoor tracking when non-line-of-sight components are significant. The probabilistic filtering algorithms require a light intensity map that is collected as a fingerprint map prior to tracking. The effect of unpredictable shadowing as the conditions change is examined in a scenario where one of the lamps has been shadowed. Our algorithm tracks the user equipment with less error than trilateration. The results also show that the tracking accuracy for our algorithms is on the order of the grid resolution of the fingerprint map for high signal-to-noise ratio environments.