Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

JLGBMLoc: A Novel High-precision Indoor Localization Method Based on LightGBM

Version 1 : Received: 7 March 2021 / Approved: 8 March 2021 / Online: 8 March 2021 (12:23:36 CET)

A peer-reviewed article of this Preprint also exists.

Yin, L.; Ma, P.; Deng, Z. JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM. Sensors 2021, 21, 2722. Yin, L.; Ma, P.; Deng, Z. JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM. Sensors 2021, 21, 2722.

Journal reference: Sensors 2021, 21, 2722
DOI: 10.3390/s21082722

Abstract

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

Subject Areas

Indoor Localization; Wi-Fi Fingerprint; Denoising Auto-encoder; JLGBMLoc

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.