Indoor UAV Localization using Manifold Alignment with Mobile AP Detection

Abstract

Due to the rapid development of indoor Unmanned Aerial Vehicles (UAVs) in recent years, the localization of indoor UAVs has become a focus of attention in UAVs applications. Among them, the Wireless Local Area Network (WLAN) based localization approach has become an effective means to achieve indoor localization due to the widely-deployed WLAN infrastructure. At the same time, with the increased use of WLAN module in the state-of-the-art mobile devices, various types of mobile WLAN Access Points (APs) exist in indoor environment. In this circumstance, the mobile WLAN APs deteriorates localization accuracy since their associated Received Signal Strength (RSS) data become unstable with the variation of locations. To address this problem, a new approach based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to detect mobile WLAN APs for manifold alignment localization of UAVs. Specifically, first of all, the DBSCAN is conducted at the Reference Points (RPs) on motion paths to detect mobile APs. Second, the RSS data from mobile APs are removed from the database to enhance the location-dependency of RSS data used for the localization. Third, the concept of augmentation process is considered in manifold alignment to achieve satisfactory localization accuracy. Finally, the extensive experimental results show that the proposed system performs better in localization accuracy compared with the existing CIMLoc and WILL under the presence of mobile WLAN APs.