Abstract
Indoor localization is of great importance in pervasive applications and RSS fingerprint is known as a quite effective indoor location method. Floor attenuation might not give enough margin discrepancy to classify two neighboring floors, such as windows nearby or ring structure. Fingerprint location using the nearest Euclidean distance to the reference point can be interfered by the neighboring floor. In this paper, a multifloor localization framework with floor identification is proposed. The discriminative floor model is trained to maximize between-class scatter and floor identification is triggered by stair walk and elevator events. In experiments, a real dataset is collected in the building of six floors to evaluate our method. The results show that our method can identify accurate location in multifloor environment.
Highlights
Indoor localization is of great importance in pervasive applications
Most localization methods based on Wi-Fi RSS have been proposed in the past decades [1,2,3,4,5]. Typical procedure of these methods is RSS fingerprints (e.g., Wi-Fi signal strengths from multiple access points) which have been recorded at preset locations and a reference fingerprint database is built
The results show that Fisher’s Linear Discriminant (FLD) archives 94.3% accuracy in floor identification
Summary
Indoor localization is of great importance in pervasive applications. Most localization methods based on Wi-Fi RSS have been proposed in the past decades [1,2,3,4,5]. Typical procedure of these methods is RSS fingerprints (e.g., Wi-Fi signal strengths from multiple access points) which have been recorded at preset locations and a reference fingerprint database is built. Fingerprint-based indoor localization works well in signal floor environment.
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