Semester of Graduation

Fall 2022

Degree

Master of Electrical Engineering (MEE)

Department

Division of Electrical and Computer Engineering

Document Type

Thesis

Abstract

Indoor localization of human objects has many important applications nowadays. Proposed here is a new device free approach where all the transceiver devices are fixed in an indoor environment so that the human target doesn't need to carry any transceiver device with them. This work proposes radio-frequency fingerprinting for the localization of human targets which makes this even more convenient as radio-frequency wireless signals can be easily acquired using an existing wireless network in an indoor environment. This work explores different avenues for optimal and effective placement of transmitter devices for better localization. In this work, an experimental environment is simulated using the popular software Feko. The indoor geometry under study is first divided into several zones and then the received signal-strength indicators (RSSIs) are measured by the receiving antennae which serve as input features to our designed innovative machine-learning model to identify within which zone the target is. Our proposed machine-learning model, a multi-resolution random-forest classifier is composed of a cascade architecture that integrates and distills learned results over various zoning resolutions. The proposed new multi-resolution approach greatly outperforms the existing random-forest classifier. The average Euclidean-distance error resulting from our proposed new technique is 1.25 meters.

Date

10-28-2022

Committee Chair

Wu, Hsiao-Chun

DOI

10.31390/gradschool_theses.5664

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