상세정보
학술대회
A Novel Wi-Fi AP Localization Method Using Monte Carlo Path-loss Model Fitting Simulation
- 발행일
- 201309
- 출처
- International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC) 2013, pp.3487-3491
- 협약과제
- 13PC1100, 5m 정밀도의 증강현실 서비스 지원 LBS 플랫폼 개발, 박상준
- 초록
- Wi-Fi-based localization is one of the most promising technologies for indoor location-based services. However, it is still a difficult task to construct a positioning database to provide high accuracy and vast coverage, especially in probabilistic-based algorithms such as fingerprint-based localization. On the other hand, positioning methods that use the location database of the positioning infrastructure, such as the weighted centroid method, can support terminal-based localization due to lower computational complexity and small database size. Terminal-based positioning has the advantages not only of protecting personal location information, but also of maintaining the network topology among the terminals. The only weak point for positioning database-based methods is that the accurate location of the infrastructure should be known in advance. In this paper, we propose a novel algorithm to estimate the location of infrastructure, especially for Wi-Fi access points. We developed and employed a smartphone application to collect Wi-Fi signals by just walking around the test area. Then, the locations of Wi-Fi access points are estimated efficiently and accurately by selection of the maximum likelihood position that has the most similar path-loss model corresponding to the signal acquisition points. The estimated locations are processed as infrastructure database to support terminal-based positioning. The simulation and experiment result validate the feasibility of the proposed algorithm. The estimated location of access points is within 10 m accuracy in most cases and also the terminal-based positioning results achieve competitive performance comparing with other positioning methods. © 2013 IEEE.
- KSP 제안 키워드
- Access point, Accurate location, Centroid method, Competitive performance, High accuracy, Indoor Location-based services, Localization method, Location database, Location information(GPS), Lower computational complexity, Monte carlo