Jie Wang, Xiaoyun Zhang, Qinghua Gao, Hongyu Wang, Minglu Jin
{"title":"基于粒子滤波的无线传感器网络设备自由定位","authors":"Jie Wang, Xiaoyun Zhang, Qinghua Gao, Hongyu Wang, Minglu Jin","doi":"10.1109/ICAWST.2011.6163170","DOIUrl":null,"url":null,"abstract":"This paper proposes an approach for tracking targets in wireless sensor networks without the need of equipping the target with a wireless device. Based on the shadowing effect caused by the target, we adopt the variation of the received signal strength measurements between the node pairs to build the observation likelihood function of the target, and utilize the particle filter framework to realize the target localization and tracking. Meanwhile, to make the localization method applicable for the computational and power resource limited wireless sensor networks, we propose a scheme to wake up a subset of nodes participating in the measuring and select a subset of outstanding wireless link measurements participating in the location estimation. Experimental results demonstrate the effectiveness of our approach.","PeriodicalId":126169,"journal":{"name":"2011 3rd International Conference on Awareness Science and Technology (iCAST)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Device free localization with wireless sensor networks based on particle filter\",\"authors\":\"Jie Wang, Xiaoyun Zhang, Qinghua Gao, Hongyu Wang, Minglu Jin\",\"doi\":\"10.1109/ICAWST.2011.6163170\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an approach for tracking targets in wireless sensor networks without the need of equipping the target with a wireless device. Based on the shadowing effect caused by the target, we adopt the variation of the received signal strength measurements between the node pairs to build the observation likelihood function of the target, and utilize the particle filter framework to realize the target localization and tracking. Meanwhile, to make the localization method applicable for the computational and power resource limited wireless sensor networks, we propose a scheme to wake up a subset of nodes participating in the measuring and select a subset of outstanding wireless link measurements participating in the location estimation. Experimental results demonstrate the effectiveness of our approach.\",\"PeriodicalId\":126169,\"journal\":{\"name\":\"2011 3rd International Conference on Awareness Science and Technology (iCAST)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 3rd International Conference on Awareness Science and Technology (iCAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAWST.2011.6163170\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 3rd International Conference on Awareness Science and Technology (iCAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAWST.2011.6163170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Device free localization with wireless sensor networks based on particle filter
This paper proposes an approach for tracking targets in wireless sensor networks without the need of equipping the target with a wireless device. Based on the shadowing effect caused by the target, we adopt the variation of the received signal strength measurements between the node pairs to build the observation likelihood function of the target, and utilize the particle filter framework to realize the target localization and tracking. Meanwhile, to make the localization method applicable for the computational and power resource limited wireless sensor networks, we propose a scheme to wake up a subset of nodes participating in the measuring and select a subset of outstanding wireless link measurements participating in the location estimation. Experimental results demonstrate the effectiveness of our approach.