Decentralized indoor wireless localization using compressed sensing of signal-strength fingerprints
Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Beacon selection for localisation in IEEE 802.11 wireless infrastructure
International Journal of Ad Hoc and Ubiquitous Computing
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
Beacon selection for localisation in IEEE 802.11 wireless infrastructure
International Journal of Ad Hoc and Ubiquitous Computing
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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This paper presents a novel approach to building a WLAN-based location fingerprinting system. Our algorithm intelligently transforms received signal strength (RSS) into principal components (PCs) such that the information of all access points (APs) is more efficiently utilized. Instead of selecting APs, the proposed technique replaces the elements with a subset of PCs to simultaneously improve the accuracy and reduce the online computation. Our experiments are conducted in a realistic WLAN environment. The results show that the mean error is reduced by 33.75 percent, and the complexity by 40 percent, as compared to the existing methods. Moreover, several benefits of our algorithm are demonstrated, such as requiring fewer training samples and enhancing the robustness to RSS anomalies.