Fast fingerprints construction via GPR of high spatial-temporal resolution with sparse RSS sampling in indoor localization

Effective indoor localization largely relies on the fingerprint database (model) of Received Signal Strength (RSS) in connection with Radio Frequency sources, such as the most widely used Bluetooth Low Energy (BLE) iBeacons. RSSs exhibit significant random variations in both the spatial and temporal domains. It is a notoriously onerous and challenging task to construct the fingerprint database for accurate localization, as the BLE RSSs must be captured via a full space scan from one point to another every few meters in a certain period of time. In order to tackle this problem, this study proposes an approach to fast fingerprints construction that only requires a sparse sampling of RSS of the space. First, a smartphone records the time series of RSS over a designated path, and a radio map for the path is then generated by a spatio-temporal mapping method using the Pedestrian Dead Reckoning algorithm. Second, the radio map of the entire space can be obtained via Gauss Process Regression (GPR), with outliers reduced to improve the reliability of the fingerprint database. Experiments have been performed in an underground carpark (38 m ×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 14 m), and the experimental results indicate that the proposed approach can construct the fingerprint database 300% faster than the conventional approach does. The localization accuracy of both approaches is quite similar (80% error in 2.8 m). The proposed approach offers potential for the construction of a large-scale fingerprint database for a wide-area Location Based Service (LBS) of Smart City indoor and outdoor integration, where big RSS data processing is a must.