Work

Improving Indoor WiFi Localization by Using Machine Learning Techniques

Year: 2024

Type: article

Abstract: Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

Source: Sensors

DOI: 10.3390/s24196293

Authors: Hanieh Esmaeili Gorjan, Víctor P. Gil Jiménez

Institution: Universidad Carlos III de Madrid

Language: English

Topic: Indoor and Outdoor Localization Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955