Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point-Regression Versus Classification

TL;DR

This paper introduces a deep learning-based indoor localization method using logistic regression and CRLB-assisted training, achieving sub-meter accuracy with low computational overhead in single WiFi AP environments.

Contribution

It proposes a novel regression-based deep learning approach for indoor localization, contrasting with traditional classification methods, and demonstrates improved robustness and accuracy.

Findings

Achieves 0.97m median error in laboratory tests

Maintains low online prediction overhead

Outperforms classification-based methods in robustness

Abstract

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cram\'er-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.