상세정보
학술대회
Performance Analysis of Manifold Learning Methods for Radio Map Construction
- 발행일
- 201707
- 출처
- International Conference on Ubiquitous and Future Networks (ICUFN) 2017, pp.587-591
- 협약과제
- 17ZH1500, 단말 근접 실시간 스마트 서비스추천플랫폼 기술 개발, 정기숙
- 초록
- Wifi fingerprinting is an appealing method for indoor positioning as it can utilizes available wireless infrastructures while retaining an acceptable level of accuracy. For the method to work, radio map of target environment has to be constructed in advance. The process is laborious and time consuming as signal strength has to be collected from mobile devices at a large number of reference locations. An attractive idea is to harness unlabeled wireless signal data from crowd to help construct the radio map. Various manifold learning methods have been employed for this purpose. However, results are not comparable as they are highly dependent on experiment environment. In this works, we implement four different manifold learning methods for the radio map construction task and report the localization performance of the resulting map on a simulated data and a public real dataset.
- KSP 제안 키워드
- Indoor Positioning, Learning methods, Level of accuracy, Localization performance, Manifold learning, Mobile devices, Performance analysis, Radio map construction, Signal Strength, Simulated data, WiFi fingerprinting