Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point-Regression Versus Classification
PubDate: Nov 2019
Teams: Shanghai Institute for Advanced Communication and Data Science；National and Kapodistrian University of Athens Panepistimiopolis Ilissia；Hong Kong University of Science and Technology
Writers: Chenlu Xiang, Shunqing Zhang, Shugong Xu, Xiaojing Chen, George C. Alexandropoulos, Vincent K. N. Lau
Abstract
Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.
