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Title:Incoherent scatter modeling of Jicamarca radar spectra
Author(s):Aggarwal, Deepanshu
Advisor(s):Kudeki, Erhan
Department / Program:Electrical and Computer Engineering
Discipline:Electrical & Computer Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Jicamarca
Radar spectra
k-Nearest Neighbors (KNN) Machine Learning
Ionosphere
Collision Frequency
Machine Learning
Electron ion collision
Abstract:This thesis describes a project on the modeling of spectral characteristics of electron density irregularities of the topside equatorial ionosphere probed by the Jicamarca Incoherent Scatter Radar (ISR) located near Lima, Peru. The topside equatorial ionosphere is a multi-ion plasma and the spectrum of its electron density irregularities can be modeled by extending the single-ion spectral model developed by Kudeki and Milla (2011) for a collisional equatorial F-region ionosphere. This single-ion model of Kudeki and Milla captures the essential physics of the equatorial F-region ionosphere where random displacements of the dominant oxygen ions are characterized as a Brownian-motion process, while electron displacements are non-Brownian and described in terms of a numerical library (constructed using a Monte-Carlo approach) of single-electron ACFs (autocorrelation functions) parametrized by five state parameters of the F-region consisting of ionospheric electron density, geomagnetic flux density, electron and ion temperatures, and the deviation angle of the radar boresight direction from the plane perpendicular to the geomagnetic field, the so-called magnetic aspect angle. While the extension of the model to the multi-ion case is straightforward, the discrete nature of the numerical electron ACF library defined over a grid of input parameters precludes the evaluation of the extended model with arbitrary and continuously varying input parameters. To overcome this difficulty a machine learning (ML) based interpolation procedure is developed. The thesis describes the ML algorithm, the associated training and testing steps, and finally presents a suite of examples of multi-ion IS spectra obtained with the extended model.
Issue Date:2014-01-16
URI:http://hdl.handle.net/2142/46790
Rights Information:Copyright 2013 Deepanshu Aggarwal
Date Available in IDEALS:2014-01-16
Date Deposited:2013-12


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