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

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