Liqiong Chang, Dingyi Fang, Zhe Yang, Xiaojiang Chen, Ju Wang, Weike Nie, Tianzhang Xing
{"title":"海报摘要:EIL——一种与环境无关的无设备被动定位方法","authors":"Liqiong Chang, Dingyi Fang, Zhe Yang, Xiaojiang Chen, Ju Wang, Weike Nie, Tianzhang Xing","doi":"10.1109/IPSN.2014.6846768","DOIUrl":null,"url":null,"abstract":"Most previous Device-free Passive Localization (DFL) methods are learning based and they assume the distribution of Received Radio Signal (RSS) distorted by an object is fixed across time. However, the signals significantly vary over time and the pre-obtained radio map (or prior knowledge) outdated in the localization phase, thus causing the localization accuracy decrease. To cope with this problem, this poster proposes, EIL, an environment-independent DFL approach which can improve the system robustness and localization accuracy by eliminating the interference of environment on RSS over time in both the training phase and the localization phase. Through both the extensive experiments and simulations, EIL keeps a range of 0.5m to 0.6m localization errors for 90% locations over time.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"266 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Poster abstract: EIL — An environment-independent Device-free Passive Localization approach\",\"authors\":\"Liqiong Chang, Dingyi Fang, Zhe Yang, Xiaojiang Chen, Ju Wang, Weike Nie, Tianzhang Xing\",\"doi\":\"10.1109/IPSN.2014.6846768\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most previous Device-free Passive Localization (DFL) methods are learning based and they assume the distribution of Received Radio Signal (RSS) distorted by an object is fixed across time. However, the signals significantly vary over time and the pre-obtained radio map (or prior knowledge) outdated in the localization phase, thus causing the localization accuracy decrease. To cope with this problem, this poster proposes, EIL, an environment-independent DFL approach which can improve the system robustness and localization accuracy by eliminating the interference of environment on RSS over time in both the training phase and the localization phase. Through both the extensive experiments and simulations, EIL keeps a range of 0.5m to 0.6m localization errors for 90% locations over time.\",\"PeriodicalId\":297218,\"journal\":{\"name\":\"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks\",\"volume\":\"266 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPSN.2014.6846768\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPSN.2014.6846768","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster abstract: EIL — An environment-independent Device-free Passive Localization approach
Most previous Device-free Passive Localization (DFL) methods are learning based and they assume the distribution of Received Radio Signal (RSS) distorted by an object is fixed across time. However, the signals significantly vary over time and the pre-obtained radio map (or prior knowledge) outdated in the localization phase, thus causing the localization accuracy decrease. To cope with this problem, this poster proposes, EIL, an environment-independent DFL approach which can improve the system robustness and localization accuracy by eliminating the interference of environment on RSS over time in both the training phase and the localization phase. Through both the extensive experiments and simulations, EIL keeps a range of 0.5m to 0.6m localization errors for 90% locations over time.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
