Indoor WiFi Localization with a Dense Fingerprint Model

Abstract—WiFi-based localization is a popular approach for positioning a WiFi-enabled device in an indoor environment. Most implementations rely on querying fingerprint databases, created by stop and go sampling of WiFi signals at discrete locations used as reference points. In this paper, we propose an approach for rapid creation of a dense WiFi fingerprint database using a human operated ambulatory backpack and a single walkthrough and data collect in an indoor environment. In addition, we present and compare the performance of 4 algorithms for localizing mobile devices based on the collected fingerprints that take advantage of the dense database. We show that it is possible to achieve mean error of 2.8 meters with 90th percentile of 5.0 meters using one of our algorithms. Keywords—indoor localization; WiFi localization; WiFi fingerprints