{"title":"使用AP逆位置估计的CNN定位","authors":"S. Aikawa, Shinichiro Yamamoto, Takuma Muramatsu","doi":"10.1109/CAMA47423.2019.8959663","DOIUrl":null,"url":null,"abstract":"This contribution focuses on indoor localization by Finger Print method using RSSI of wireless LAN access point (AP). In recent years, there are lively animated discussions on the localization methods using Deep Learning. We proposed Finger Print based on the Convolutional Neural Network (CNN). Establish the adjacency relationship among APs as a two-dimensional model and use it to make the CNN model for Finger Print localization. In order to confirm the improvement of the localization accuracy by this proposal, we verified by experimental data.","PeriodicalId":170627,"journal":{"name":"2019 IEEE Conference on Antenna Measurements & Applications (CAMA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"CNN Localization using AP Inverse Position Estimation\",\"authors\":\"S. Aikawa, Shinichiro Yamamoto, Takuma Muramatsu\",\"doi\":\"10.1109/CAMA47423.2019.8959663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This contribution focuses on indoor localization by Finger Print method using RSSI of wireless LAN access point (AP). In recent years, there are lively animated discussions on the localization methods using Deep Learning. We proposed Finger Print based on the Convolutional Neural Network (CNN). Establish the adjacency relationship among APs as a two-dimensional model and use it to make the CNN model for Finger Print localization. In order to confirm the improvement of the localization accuracy by this proposal, we verified by experimental data.\",\"PeriodicalId\":170627,\"journal\":{\"name\":\"2019 IEEE Conference on Antenna Measurements & Applications (CAMA)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Conference on Antenna Measurements & Applications (CAMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMA47423.2019.8959663\",\"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 Conference on Antenna Measurements & Applications (CAMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMA47423.2019.8959663","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CNN Localization using AP Inverse Position Estimation
This contribution focuses on indoor localization by Finger Print method using RSSI of wireless LAN access point (AP). In recent years, there are lively animated discussions on the localization methods using Deep Learning. We proposed Finger Print based on the Convolutional Neural Network (CNN). Establish the adjacency relationship among APs as a two-dimensional model and use it to make the CNN model for Finger Print localization. In order to confirm the improvement of the localization accuracy by this proposal, we verified by experimental data.