{"title":"基于几何模型的概率地磁指纹低功耗方位估计","authors":"Johannes Meyer, Lars Klitzke, Gerd von Cölln","doi":"10.1109/INDIN41052.2019.8972227","DOIUrl":null,"url":null,"abstract":"This work presents a new approach to estimate the orientation of wireless sensor nodes (WSN) using geomagnetic sensors. The main contribution is a new algorithm for supervised orientation estimation using geomagnetic fingerprinting. Combined with hierarchical sensing our approach leads to a significant reduction of power consumption.","PeriodicalId":260220,"journal":{"name":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Probabilistic Geomagnetic Fingerprinting for Low-Power Orientation Estimation utilising Geometric Models\",\"authors\":\"Johannes Meyer, Lars Klitzke, Gerd von Cölln\",\"doi\":\"10.1109/INDIN41052.2019.8972227\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a new approach to estimate the orientation of wireless sensor nodes (WSN) using geomagnetic sensors. The main contribution is a new algorithm for supervised orientation estimation using geomagnetic fingerprinting. Combined with hierarchical sensing our approach leads to a significant reduction of power consumption.\",\"PeriodicalId\":260220,\"journal\":{\"name\":\"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN41052.2019.8972227\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN41052.2019.8972227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Probabilistic Geomagnetic Fingerprinting for Low-Power Orientation Estimation utilising Geometric Models
This work presents a new approach to estimate the orientation of wireless sensor nodes (WSN) using geomagnetic sensors. The main contribution is a new algorithm for supervised orientation estimation using geomagnetic fingerprinting. Combined with hierarchical sensing our approach leads to a significant reduction of power consumption.
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