Files in this item

FilesDescriptionFormat

application/pdf

application/pdfYE-THESIS-2021.pdf (9MB)Restricted to U of Illinois
(no description provided)PDF

Description

Title:Estimation of ion drift velocity vector in F region ionosphere based on incoherent scattered pulse data using machine learning technique
Author(s):Ye, Bingqian
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):plasma drift velocity
incoherent scatter radar
machine learning
Abstract:This thesis presents work on estimating the vector drift velocities of the plasma at different altitudes in the F region of the ionosphere using machine learning techniques. The computation is based on the radar data acquired with the ALTAIR (Automatic Radar Plotting Aid (ARPA) Long-Range Tracking and Instrumentation Radar) incoherent scatter radar (ISR). The line-of-sight (LOS) Doppler velocity of radar backscattered echoes can be obtained by estimating the phase slope of the auto-correlation function (ACF) of backscattered signals. In order to improve accuracy, a machine learning algorithm, DBSCAN (density-based spatial clustering of applications with noise), is used to distinguish the data segments from the noise segments. The LOS velocities of backscattered pulses are projections of the drift velocity vectors on LOS direction unit vector. A system of linear equations based on geometry will be established to estimate the velocity vectors. This thesis will also describe the procedure of formulating the linear equations at each height and include a rank 2 regularization to solve the system of linear equations on a realistic basis.
Issue Date:2021-04-30
Type:Thesis
URI:http://hdl.handle.net/2142/110761
Rights Information:Copyright 2021 Bingqian Ye
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


This item appears in the following Collection(s)

Item Statistics