Ayoub Idelhaj

Graduation Semester and Year

2021

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Yan Wan

Abstract

This thesis investigates model-based and data-driven approaches for indoor localization using the Received Signal Strength Indicator (RSSI) of Wi-Fi signals. We study multiple model-based indoor localization approaches, including the free space path loss model, the log-distance path loss model, the International Telecommunication Union (ITU) model, and a nonlinear regression model. We examine their indoor localization accuracy using raw RSSI values, and filter RSSI values passed through a Moving Average filter and a Kalman filter. For data driven approaches, we employ a family of Extreme Learning Machine (ELM) algorithms including Basic-ELM, Online Sequential-ELM (OS-ELM), Hierarchical-ELM (H-ELM), and Kernel-ELM (K-ELM), to find the indoor position. We provide simulation results comparing the performances of both the Machine-learning based approaches and model-based approaches in terms of localization error to identify the algorithms with the lowest localization error.

Keywords

Received signal strength indicator (RSSI), International telecommunication union (ITU), Extreme learning machine (ELM)

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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