Robust Sub-meter Level Indoor Localization - A Logistic Regression Approach

TL;DR

This paper introduces a logistic regression-based deep learning approach for indoor WiFi localization, achieving sub-meter accuracy with reduced computational overhead compared to traditional methods.

Contribution

It proposes a novel logistic regression scheme within a deep learning framework for WiFi localization, improving accuracy and efficiency over existing classification-based models.

Findings

Achieves 97.2cm median error in laboratory environment

Maintains reasonable online prediction overhead

Outperforms traditional classification-based localization methods

Abstract

Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems.