Abstract
Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.
Highlights
Indoor localization has gained popularity in recent years due to the availability of mobile devices and increased demand for a solution for positioning and navigation for indoor spaces
The Received Signal Strength (RSS) fingerprinting based localization systems are usually implemented in the 802.11 wireless local area network to determine user location by measuring frames sent from different access points (APs)
The system has to build a database of fingerprints, where each fingerprint is the RSS information collected by measuring received signal strength at reference point from access points
Summary
Indoor localization has gained popularity in recent years due to the availability of mobile devices and increased demand for a solution for positioning and navigation for indoor spaces. Wi-Fi signals can be used for indoor localization (Liu et al, 2007). Wi-Fi technologies are found everywhere in various forms; it gives the advantage to this technology to provide indoor location-based service. The RSS fingerprinting based localization systems are usually implemented in the 802.11 wireless local area network to determine user location by measuring frames sent from different access points (APs). Localization/positioning techniques that use Wi-Fi RSS measurement have two phases. The system has to build a database of fingerprints, where each fingerprint is the RSS information collected by measuring received signal strength at reference point from access points. RSS signals measurements from APs are compared from the fingerprint database using mathematical modeling methods to estimate user location (Zegeye et al, 2016)
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