Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.
Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning
01.04.2021
4597427 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Ensembling clocks of Global Positioning System
IEEE | 1990
|RSSI-Based Fingerprint Positioning System for Indoor Wireless Network
British Library Conference Proceedings | 2014
|RSSI-Based Fingerprint Positioning System for Indoor Wireless Network
Springer Verlag | 2014
|Removing useless APs and fingerprints from WiFi indoor positioning radio maps
BASE | 2013
|