  The proliferation of data-demanding machine learning methods has brought to
light the necessity for methodologies which can enlarge the size of training
datasets, with simple, rule-based methods. In-line with this concept, the
fingerprint augmentation scheme proposed in this work aims to augment
fingerprint datasets which are used to train positioning models. The proposed
method utilizes fingerprints which are recorded in spacial proximity, in order
to perform fingerprint augmentation, creating new fingerprints which combine
the features of the original ones. The proposed method of composing the new,
augmented fingerprints is inspired by the crossover and mutation operators of
genetic algorithms. The ProxyFAUG method aims to improve the achievable
positioning accuracy of fingerprint datasets, by introducing a rule-based,
stochastic, proximity-based method of fingerprint augmentation. The performance
of ProxyFAUG is evaluated in an outdoor Sigfox setting using a public dataset.
The best performing published positioning method on this dataset is improved by
40% in terms of median error and 6% in terms of mean error, with the use of the
augmented dataset. The analysis of the results indicate a systematic and
significant performance improvement at the lower error quartiles, as indicated
by the impressive improvement of the median error.
