Low-effort place recognition with WiFi fingerprints using deep learning

TL;DR

This paper presents a deep learning approach using stacked autoencoders for WiFi fingerprint-based indoor localization, significantly reducing manual effort while maintaining high classification accuracy on a public dataset.

Contribution

It introduces a deep neural network architecture with stacked autoencoders for building and floor classification in WiFi-based indoor localization, streamlining system design.

Findings

Achieves high classification accuracy on UJIIndoorLoc dataset

Reduces feature space complexity with stacked autoencoders

Outperforms some existing solutions in accuracy

Abstract

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions.