Biljana Risteska Stojkoska, Jordan Palikrushev, K. Trivodaliev, S. Kalajdziski
{"title":"Indoor localization of unmanned aerial vehicles based on RSSI","authors":"Biljana Risteska Stojkoska, Jordan Palikrushev, K. Trivodaliev, S. Kalajdziski","doi":"10.1109/EUROCON.2017.8011089","DOIUrl":null,"url":null,"abstract":"Nowadays, finding the Unmanned Aerial Vehicle (UAV) position in the absence of GPS is attractive and challenging problem in the research community. In this paper, we present a novel algorithm for mini UAV indoor localization based on distance measurements between the UAV and the existing infrastructure consisting of WiFi Access Points. Our algorithm uses two well-known techniques from the literature: Multi-dimensional Scaling (MDS) and Weighted Centroid Localization (WCL). Through extensive simulations we have shown that our algorithm is very suitable for indoor localization of mini UAVs. For small radio-range error, our algorithm exhibits a small localization error of less than 5% of the radio range.","PeriodicalId":114100,"journal":{"name":"IEEE EUROCON 2017 -17th International Conference on Smart Technologies","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE EUROCON 2017 -17th International Conference on Smart Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EUROCON.2017.8011089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
Nowadays, finding the Unmanned Aerial Vehicle (UAV) position in the absence of GPS is attractive and challenging problem in the research community. In this paper, we present a novel algorithm for mini UAV indoor localization based on distance measurements between the UAV and the existing infrastructure consisting of WiFi Access Points. Our algorithm uses two well-known techniques from the literature: Multi-dimensional Scaling (MDS) and Weighted Centroid Localization (WCL). Through extensive simulations we have shown that our algorithm is very suitable for indoor localization of mini UAVs. For small radio-range error, our algorithm exhibits a small localization error of less than 5% of the radio range.