{"title":"A fingerprinting indoor localization algorithm based deep learning","authors":"G. Felix, Mario Siller, Ernesto Navarro-Alvarez","doi":"10.1109/ICUFN.2016.7536949","DOIUrl":null,"url":null,"abstract":"Fingerprinting in essence uses a machine to infer physicals locations from radio map data. This machines are usually either probabilistic and neural networks consisting of one layer. In this propose we use deeper machines (DNN, DBN and GB-DBN) to increase the estimation accuracy and reduce generalization error on dynamic indoor environment. Also we investigated the impact of pre-training algorithm on fingerprinting indoor location systems. Experimental results demonstrate that deep models provide an efficient generalization performance on indoor environments. They have the disadvantage that demand high processing resources when they are trained on off-line phase, however, deep models are swift to predict during on-line phase.","PeriodicalId":403815,"journal":{"name":"2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"81","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUFN.2016.7536949","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 81
Abstract
Fingerprinting in essence uses a machine to infer physicals locations from radio map data. This machines are usually either probabilistic and neural networks consisting of one layer. In this propose we use deeper machines (DNN, DBN and GB-DBN) to increase the estimation accuracy and reduce generalization error on dynamic indoor environment. Also we investigated the impact of pre-training algorithm on fingerprinting indoor location systems. Experimental results demonstrate that deep models provide an efficient generalization performance on indoor environments. They have the disadvantage that demand high processing resources when they are trained on off-line phase, however, deep models are swift to predict during on-line phase.