Environment-Aware Regression for Indoor Localization Based on WiFi Fingerprinting

Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples and proposes a new model for received signal strength regression. The new model creates vectors that describe the presence of obstacles between an access point and the collected samples. The vectors, the distance between the access point and the positions of the samples, and the collected, are used to train a Support Vector Regression. The experiments included some relevant analyses and showed that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.

Automated summary

Data enrichment through interpolation or regression is a common approach for dealing with sample collection for Indoor Localization with WiFi fingerprinting. This paper introduces a new model for received signal strength regression, which creates vectors to describe obstacles between access points and collected samples, and utilizes Support Vector Regression for training. Experimental results show that the proposed model improves received signal strength regression in terms of regression residuals and positioning accuracy.