We report the results of our investigation on the use of deep neural networks (DNNs) for building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting. We propose a new DNN architecture based on a stacked autoencoder for feature space dimension reduction and a feed-forward classifier for multi-label classification with arg max functions to convert multi-label classification results into multi-class classification ones. We also demonstrate a prototype system for floor-level location estimation using received signal strengths measured on XJTLU campus. Our results show the strengths of DNN-based approaches, providing near state-of-the-art performance with less parameter tuning and higher scalability.

我们报告了使用深度神经网络（DNN）进行基于Wi-Fi指纹识别的建筑物/楼层分类和楼层位置估计的调查结果。我们提出了一种新的DNN架构，该架构基于用于减少特征空间尺寸的堆叠式自动编码器和具有arg max函数的多标签分类的前馈分类器，以将多标签分类结果转换为多类分类结果。我们还演示了使用XJTLU校园测量的接收信号强度进行楼层位置估计的原型系统。我们的结果显示了基于DNN的方法的优势，它提供了近乎最新的性能，具有更少的参数调整和更高的可扩展性。