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A deep learning approach to fingerprinting indoor localization solutions

机译：指纹室内定位解决方案的深度学习方法

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摘要

Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. Considered as a data-driven approach, many modern data analytics can be used to improve its performance. In this paper, we propose tow learning algorithms, namely a deep learning architecture for regression and Support Vector Machine (SVM) for classification, to output the estimated location directly from the measured fingerprints. The design issues of the proposed neural network is discussed including the training algorithm, regularization and hyperparameter selection. It is discussed how data augmentation methods can be utilized to extend the measurements. The deep learning approach can be used to save the data collection time significantly using a pre-trained model. Moreover the run-time complexity is significantly reduced. The numerical analysis show that in some case, only 10 percent of original training database is enough to get acceptable performance on a pre-trained model.
机译：指纹本地化解决方案（FPS）由于其良好的性能和最少的环境信息要求而受到广泛欢迎。被认为是一种数据驱动的方法，许多现代数据分析都可以用来提高其性能。在本文中，我们提出了两种学习算法，即用于回归的深度学习体系结构和用于分类的支持向量机（SVM），以直接从测量的指纹输出估计的位置。讨论了所提出的神经网络的设计问题，包括训练算法，正则化和超参数选择。讨论了如何利用数据增强方法来扩展测量。深度学习方法可用于使用预先训练的模型来显着节省数据收集时间。此外，运行时的复杂性大大降低了。数值分析表明，在某些情况下，仅原始训练数据库的10％就足以在预训练模型上获得可接受的性能。

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