Indoor localization has been a hot area of research over the past two decades. Since its advent, it has been steadily utilizing the emerging technologies to improve accuracy, and machine learning has been at the heart of that. Machine learning has been increasingly used in fingerprint-based indoor localization to replace or emulate the radio map that is used to predict locations given a location signature. The prediction quality of a machine learning model primarily depends on how well the model was trained, which relies on the amount and quality of data used to train it. Data augmentation has been used to improve quality of the trained models by synthetically producing more training data, and several approaches were used in the literature that tackles the problem of lack of training data from different angles. In this paper, we propose DataLoc+, a data augmentation technique for room-level indoor localization that combines different approaches in a simple algorithm. We evaluate the technique by comparing it to the typical direct snapshot approach using data collected from a field experiment conducted in a hospital. Our evaluation shows that the model trained using the proposed technique achieves higher accuracy. We also show that the technique adapts to larger problems using a limited dataset while maintaining high accuracy.
翻译:在过去二十年中,室内本地化一直是研究的一个热点领域。自其出现以来,它一直在稳步地利用新兴技术提高准确性,而机器学习正是其核心所在。机器学习越来越多地用于基于指纹的室内本地化,以取代或模仿用于预测地点的无线电地图,该无线电地图用于预测地点的定位签名。机器学习模型的预测质量主要取决于该模型所培训的程度,该模型依赖于用于培训的数据的数量和质量。数据增强一直用于通过合成制作更多的培训数据来提高经过培训的模型的质量,在解决缺乏不同角度的培训数据问题的文献中使用了几种方法。在本文件中,我们提出了DataLoc+,这是一种用于室内本地化的数据增强技术,将各种方法结合到简单的算法中。我们用在医院进行的实地实验中收集的数据来评估该技术与典型的直接快照方法进行比较。我们的评估表明,使用拟议技术培训的模型具有更高的准确性。我们还表明,该技术在使用有限的数据集的同时被应用于更大的问题。