Ensembling multiple radio maps with dynamic noise in fingerprint-based indoor positioning

• Main Author: Torres-Sospedra, Joaquín
• Publication Date: 2021
• Other Authors: Aranda, Fernando J., Alvarez, Fernando J., Quezada-Gaibor, Darwin, Silva, Ivo Miguel Menezes, Pendão, Cristiano Gonçalves, Moreira, Adriano
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/1822/82113
• topic: Indoor Positioning; Fingerprinting; Radio Map; Noisy samples; Ensemble; Science & Technology

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.