Incoherent scatter radar spectrum fitting with Arecibo Observatory data
Wu, Yulun
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/2142/97893
Description
Title
Incoherent scatter radar spectrum fitting with Arecibo Observatory data
Author(s)
Wu, Yulun
Issue Date
2017-05
Keyword(s)
incoherent scatter
signal processing
optimization
Abstract
Incoherent scatter radar (ISR) at Arecibo Observatory measures the scattering of electromagnetic
waves from random density fluctuations of ionospheric plasma particles (electrons
and ions). Information about particle temperatures, ion concentrations, and Doppler shifts
caused by particle motions can be estimated by fitting the power spectra of received scatter
signals in the frequency domain. Power spectrum estimates are derived by taking FFT
of signal samples and averaging the magnitude square of the FFTs. Power spectra include
both statistical estimation errors due to the use of finite length data sets and a characteristic
shape that depends on ionospheric parameters via a known non-linear relationship that is
exploited during the fitting process. This thesis mainly focuses on Arecibo data analysis
including spectrum estimation with raw voltage data samples, weighted least squares fitting
of the spectral estimates to double-humped spectral model of ionospheric incoherent scatter
signals, as well as discussions of potential fitting errors caused by statistical estimation errors
and overfit problems.
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.