This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/129507
Description
Title
Inferring indoor floorplans from wireless signals
Author(s)
Amballa, Chaitanya
Issue Date
2025-04-21
Director of Research (if dissertation) or Advisor (if thesis)
Roy Choudhury, Romit
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
NeRF
Indoor floorplan
Abstract
Neural Radiance Fields (NeRFs) have been remarkably successful at synthesizing novel views of 3D scenes by optimizing a volumetric scene function. This scene function models how an optical ray accumulates colors on its path and eventually delivers this color to the camera pixel it impinges upon. Radio frequency (RF) or audio signals can also be viewed as a vehicle for delivering information about the environment to a sensor. However, unlike camera pixels, an RF/audio sensor receives a mixture of signals that contains many environmental reflections. Is it still possible to infer the environment using such mixed signals? We show that with redesign, the core NeRF framework has the potential to solve this inverse problem. We focus on a specific application of inferring the indoor floorplan of a home from WiFi measurements made at multiple locations inside the home. Our inferred floorplans look promising, and benefit downstream signal prediction applications. Our work also uncovers a number of problems for continued research.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
