DNN-based Indoor Fingerprinting Localization with WiFi FTM

Paulson Eberechukwu, Hyunwoo Park, Christos Laoudias, Seppo Horsmanheimo, Sunwoo Kim

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

In this work, we present a deep neural network (DNN)-based indoor fingerprinting localization method with WiFi fine time measurements (FTM). The proposed method leverages the WiFi FTM and its variance as environment features to provide accurate location estimation. An $i$-th layer DNN structure used in this paper is implemented by back propagation using an Adam optimizer. The weights and the bias of the $l-\text{th}$ layer that minimize the loss function is computed in order to minimize the positioning mean squared error (MSE). Experimental results using real-world data obtained in a typical office setting proves the efficiency of the proposed solution. The performance of the system is remarkably improved, using the $600\times 600$ hidden layer size of the DNN, we achieved an average positioning accuracy of 0.7 m and 0.9 m for the 68-th percentiles $(1-\sigma)$ and 95-th percentiles $(2-\sigma)$ respectively.
Original language English Proceedings - 2022 23rd IEEE International Conference on Mobile Data Management, MDM 2022 IEEE Institute of Electrical and Electronic Engineers 367-371 5 978-1-6654-5176-5 978-1-6654-5177-2 https://doi.org/10.1109/MDM55031.2022.00082 Published - 9 Jun 2022 A4 Article in a conference publication 2022 23rd IEEE International Conference on Mobile Data Management (MDM) - Paphos, CyprusDuration: 6 Jun 2022 → 9 Jun 2022

Conference

Conference 2022 23rd IEEE International Conference on Mobile Data Management (MDM) 6/06/22 → 9/06/22

Keywords

• Location awareness
• Deep learning
• Backpropagation
• Neural networks
• Estimation
• Fingerprint recognition
• Time measurement
• Fingerprinting
• Indoor localization
• Deep Learning
• FTM

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