{"title":"基于一对全正则化逻辑回归的WiFi室内定位分类","authors":"Zifan Peng, Yuchen Xie, Donglin Wang, Z. Dong","doi":"10.1109/SARNOF.2016.7846746","DOIUrl":null,"url":null,"abstract":"Wi-Fi based indoor localization is gaining popularity because of the wide adoption of WiFi technologies in existing infrastructure. In order to increase the accuracy of Wi-Fi localization, we develop a novel localization method using One-to-all Regularized Logistic Regression-based Classification (ORLRC). This method is based on logistic regression. The proposed ORLRC is compared with the k-means clustering approach and achieves a location estimation accuracy of 95.8% comparing to an accuracy of 80% by the k-means clustering approach.","PeriodicalId":137948,"journal":{"name":"2016 IEEE 37th Sarnoff Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"One-to-all regularized logistic regression-based classification for WiFi indoor localization\",\"authors\":\"Zifan Peng, Yuchen Xie, Donglin Wang, Z. Dong\",\"doi\":\"10.1109/SARNOF.2016.7846746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wi-Fi based indoor localization is gaining popularity because of the wide adoption of WiFi technologies in existing infrastructure. In order to increase the accuracy of Wi-Fi localization, we develop a novel localization method using One-to-all Regularized Logistic Regression-based Classification (ORLRC). This method is based on logistic regression. The proposed ORLRC is compared with the k-means clustering approach and achieves a location estimation accuracy of 95.8% comparing to an accuracy of 80% by the k-means clustering approach.\",\"PeriodicalId\":137948,\"journal\":{\"name\":\"2016 IEEE 37th Sarnoff Symposium\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 37th Sarnoff Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SARNOF.2016.7846746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 37th Sarnoff Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SARNOF.2016.7846746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
One-to-all regularized logistic regression-based classification for WiFi indoor localization
Wi-Fi based indoor localization is gaining popularity because of the wide adoption of WiFi technologies in existing infrastructure. In order to increase the accuracy of Wi-Fi localization, we develop a novel localization method using One-to-all Regularized Logistic Regression-based Classification (ORLRC). This method is based on logistic regression. The proposed ORLRC is compared with the k-means clustering approach and achieves a location estimation accuracy of 95.8% comparing to an accuracy of 80% by the k-means clustering approach.
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