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Title:Inversion of Arecibo incoherent scatter radar coded long pulse backscatter spectra
Author(s):Wu, Yulun
Advisor(s):Kudeki, Erhan
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Incoherent scatter radar
inversion
signal processing
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 inverting the power spectra of received scatter signals to the numerially implemented ISR forward spectral model 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 inversion process. This thesis first describes the numerical implementation of the complete collisional ISR spectral model using chirp-z algorithm, and mainly focuses on Arecibo coded long pulse (CLP) data analysis, including spectrum generation and inversion of raw voltage data from two receivers of Arecibo Observatory. Regular FFT method and the multi-level chirp-z algorithm for speeding up spectrum computation are presented. Weighted least-square spectrum inversion of the spectral estimates to double-humped spectral model of ionospheric incoherent scatter signals using various inversion techniques including the inversion of ion drift velocity using measured spectra ACF are discussed.
Issue Date:2020-05-15
Type:Thesis
URI:http://hdl.handle.net/2142/108203
Rights Information:Copyright 2020 Yulun Wu
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05


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