{"title":"利用环境调频无线电RSS指纹进行室内定位:一项为期9个月的研究","authors":"A. Popleteev","doi":"10.1109/CIT.2017.57","DOIUrl":null,"url":null,"abstract":"While indoor positioning systems aspire for higher accuracy, their coverage is typically limited to buildings with dedicated hardware. A possible alternative is offered by infrastructure-free positioning methods. In particular, several studies have demonstrated feasibility of indoor positioning using broadcast FM radio signals, which are available in most populated areas worldwide. However, previous work provides little information about long-term performance of FM-based indoor localization.This paper presents a longitudinal study of FM indoor positioning based on received signal strength (RSS) fingerprinting. We evaluate system's performance on a large dataset of real-world FM signals, systematically collected in several large-scale multi-floor testbeds over the course of 9 months. We also investigate the impact of different classifiers, training schedules and fingerprint sizes on localization accuracy. The results demonstrate that well-trained FM-based system can provide reliable indoor positioning even several months after deployment.","PeriodicalId":378423,"journal":{"name":"2017 IEEE International Conference on Computer and Information Technology (CIT)","volume":"390 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Indoor Localization Using Ambient FM Radio RSS Fingerprinting: A 9-Month Study\",\"authors\":\"A. Popleteev\",\"doi\":\"10.1109/CIT.2017.57\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While indoor positioning systems aspire for higher accuracy, their coverage is typically limited to buildings with dedicated hardware. A possible alternative is offered by infrastructure-free positioning methods. In particular, several studies have demonstrated feasibility of indoor positioning using broadcast FM radio signals, which are available in most populated areas worldwide. However, previous work provides little information about long-term performance of FM-based indoor localization.This paper presents a longitudinal study of FM indoor positioning based on received signal strength (RSS) fingerprinting. We evaluate system's performance on a large dataset of real-world FM signals, systematically collected in several large-scale multi-floor testbeds over the course of 9 months. We also investigate the impact of different classifiers, training schedules and fingerprint sizes on localization accuracy. The results demonstrate that well-trained FM-based system can provide reliable indoor positioning even several months after deployment.\",\"PeriodicalId\":378423,\"journal\":{\"name\":\"2017 IEEE International Conference on Computer and Information Technology (CIT)\",\"volume\":\"390 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Conference on Computer and Information Technology (CIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIT.2017.57\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Computer and Information Technology (CIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIT.2017.57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Indoor Localization Using Ambient FM Radio RSS Fingerprinting: A 9-Month Study
While indoor positioning systems aspire for higher accuracy, their coverage is typically limited to buildings with dedicated hardware. A possible alternative is offered by infrastructure-free positioning methods. In particular, several studies have demonstrated feasibility of indoor positioning using broadcast FM radio signals, which are available in most populated areas worldwide. However, previous work provides little information about long-term performance of FM-based indoor localization.This paper presents a longitudinal study of FM indoor positioning based on received signal strength (RSS) fingerprinting. We evaluate system's performance on a large dataset of real-world FM signals, systematically collected in several large-scale multi-floor testbeds over the course of 9 months. We also investigate the impact of different classifiers, training schedules and fingerprint sizes on localization accuracy. The results demonstrate that well-trained FM-based system can provide reliable indoor positioning even several months after deployment.