{"title":"基于深度学习的WLAN指纹定位","authors":"S. Aikawa, Shinichiro Yamamoto, M. Morimoto","doi":"10.1109/APCAP.2018.8538306","DOIUrl":null,"url":null,"abstract":"Navigation applications for smartphones are poplar system, recently. Especially, WLAN Finger Print technique is suitable for indoor environment where GPS is difficult to use. This contribution describes a Finger Print Localization scheme using Deep Learning technique. First, the principle and experimental results of Finger Print using Deep Learning are described. Second, Coarse-to-Fine Localization based on SOM is proposed. A scheme to guess ahead accuracy for WLAN/GPS switching is described in the last section.","PeriodicalId":198124,"journal":{"name":"2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"WLAN Finger Print Localization using Deep Learning\",\"authors\":\"S. Aikawa, Shinichiro Yamamoto, M. Morimoto\",\"doi\":\"10.1109/APCAP.2018.8538306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Navigation applications for smartphones are poplar system, recently. Especially, WLAN Finger Print technique is suitable for indoor environment where GPS is difficult to use. This contribution describes a Finger Print Localization scheme using Deep Learning technique. First, the principle and experimental results of Finger Print using Deep Learning are described. Second, Coarse-to-Fine Localization based on SOM is proposed. A scheme to guess ahead accuracy for WLAN/GPS switching is described in the last section.\",\"PeriodicalId\":198124,\"journal\":{\"name\":\"2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APCAP.2018.8538306\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APCAP.2018.8538306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
WLAN Finger Print Localization using Deep Learning
Navigation applications for smartphones are poplar system, recently. Especially, WLAN Finger Print technique is suitable for indoor environment where GPS is difficult to use. This contribution describes a Finger Print Localization scheme using Deep Learning technique. First, the principle and experimental results of Finger Print using Deep Learning are described. Second, Coarse-to-Fine Localization based on SOM is proposed. A scheme to guess ahead accuracy for WLAN/GPS switching is described in the last section.
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