Indoor localization algorithm based on geometric deep learning
Xiaofei Kang,
Xian Liang,
Tian Wang
Abstract:Popular machine learning based fingerprint localization methods often struggle to effectively capture non-Euclidean characteristics present in fingerprint data, while geometric deep learning can effectively process such data. In this paper, we propose a geometric fingerprinting based graph neural network indoor localization algorithm (GFGNN), which is models access points (APs) and reference points (RPs) using received signal strength (RSS) fingerprint. This approach maximizes the utilization of the unstructur… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.