ProxyFAUG: Proximity-based Fingerprint Augmentation

TL;DR

ProxyFAUG introduces a proximity-based fingerprint augmentation technique inspired by genetic algorithms to enhance outdoor positioning accuracy, achieving significant error reductions using augmented datasets.

Contribution

It presents a novel, rule-based, stochastic fingerprint augmentation method that improves positioning accuracy by leveraging spatial proximity in fingerprint datasets.

Findings

40% median error reduction in outdoor positioning

6% mean error reduction with augmented data

Significant improvement at lower error quartiles

Abstract

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.