Accurate Entrance Position Detection Based on Wi-Fi and GPS Signals Using Machine Learning

TL;DR

This paper presents a machine learning-based system that combines GPS and Wi-Fi signals to accurately detect building entrances, achieving about one meter accuracy through real-world experiments.

Contribution

It introduces a novel approach integrating GPS and Wi-Fi RSS data for precise entrance detection, validated by extensive real-world testing.

Findings

Achieved approximately one meter accuracy in entrance detection

GPS signals decrease near entrances, Wi-Fi signals increase near entrances

System validated through multiple real-world experiments

Abstract

This paper aims at detecting an accurate position of the main entrance of the buildings. The proposed approach relies on the fact that the GPS signals drop significantly when the user enters a building. Moreover, as most of the public buildings provide Wi-Fi services, the Wi-Fi received signal strength (RSS) can be utilized in order to detect the entrance of the buildings. The rationale behind this paper is that the GPS signals decrease as the user gets close to the main entrance and the Wi-Fi signal increases as the user approaches the main entrance. Several real experiments have been conducted in order to guarantee the feasibility of the proposed approach. The experiment results have shown an interesting result and the accuracy of the whole system was one meter