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.


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    Title :

    Ensembling Multiple Radio Maps with Dynamic Noise in Fingerprint-based Indoor Positioning




    Publication date :

    2021-04-01


    Size :

    4597427 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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