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.
翻译:数据需求型机器学习方法的激增使人们看到,有必要采用各种方法扩大培训数据集的规模,采用简单、有章可循的方法。根据这一概念,在这项工作中提议的指纹扩增计划旨在增加用于定位模型培训的指纹数据集。拟议方法使用在距离较近的室外Sigfox环境中记录的指纹,以便进行指纹扩增,创造新的指纹,将原始数据集的特征结合起来。拟议的新指纹扩增法是由基因算法的交叉和突变操作者促成的。ProxyFAUG方法旨在通过采用基于规则、随机、近距离的指纹扩增方法,提高指纹数据集的可实现定位准确性。ProxyFAUG的性能利用公共数据集在户外Sigfox环境中进行评估。该数据集上最佳的已公布的定位方法在中值错误方面改进了40%,中值错误方面改进了6%,使用增强的数据集则改进了中值误率。对结果的分析表明,低误差中值的中值是系统性和显著的改进。