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dc.contributor.authorKlemm, Jared
dc.date.accessioned2020-02-13T21:06:34Z
dc.date.available2020-02-13T21:06:34Z
dc.date.issued2019-12
dc.identifier.urihttp://hdl.handle.net/11122/10901
dc.descriptionThesis (M.S.) University of Alaska Fairbanks, 2019en_US
dc.description.abstractGround-based radar systems are routinely used to detect the trails of ionized particles that are formed by meteoroids falling through Earth's atmosphere. The most common use for these meteor radar systems is for atmospheric wind studies of the mesosphere and lower thermosphere (80-100 km altitude). Because these meteor trails are embedded in the background winds of the middle atmosphere, atmospheric winds in that region can be measured by observing the radial velocities of the trails. There has also been a considerable amount of research over the last few decades into estimation of neutral atmospheric temperatures using the measured decay time of meteor trails. Several methods exist for estimating atmospheric temperature using meteor radar observations, but there are limitations to these approaches. This thesis focuses on examining aspects of meteor radar signal and data processing, specifically interferometry and echo classification. Interferometry using the measured signal phase differences between antennas allows for the location of meteor trails to be unambiguously determined. Classification schemes are used to identify which echoes can be modeled as underdense meteors, overdense meteors, or other potentially non-meteor echoes. Finally, based on the proposed classification scheme, this thesis examines several temperature estimation methods for both underdense and overdense echoes and discusses the current issues in this area. Preliminary results from a newly installed meteor radar at Poker Flat Research Range are also presented.en_US
dc.description.sponsorshipNSF grant AGS-1651464en_US
dc.description.tableofcontentsChapter 1: Introduction -- 1.1 Thesis objectives -- 1.2 History of meteor observations -- 1.3 Thesis overview -- Chapter 2: Meteor detection and classification -- 2.1 Introduction -- 2.2 Radar interferometry -- 2.2.1 Classical meteor radar interferometry -- 2.2.2 Complex plane interferometry -- 2.2.3 Comparison of interferometry methods -- 2.3 Meteor echo classification algorithms -- 2.3.1 Time series classification -- 2.3.2 Support vector machine classification -- 2.3.3 Skicorr classification -- 2.4 Algorithms in use for Poker Flat meteor radar processing -- Chapter 3: Poker Flat meteor radar data -- 3.1 Meteor data from November 2018 through August 2019 -- 3.2 Hourly wind data -- Chapter 4: Temperature estimation using meteor radar observations: methods and current issues -- 4.1 Estimating temperature using meteor decay time -- 4.2 Estimating temperature without density or pressure estimates -- 4.2.1 Estimating temperature with density or pressure estimates -- 4.3 Observations over Sodankyl¨a Geophysical Observatory -- 4.4 Simultaneous observations with multiple instruments -- 4.4.1 Observations on 23 December 2018 -- 4.4.2 Observations on 24 December 2018 -- 4.4.3 Summary of simultaneous observations -- Chapter 5: Conclusions and future work -- 5.1 Introduction -- 5.2 Meteor radar interferometry -- 5.3 Classification of meteors -- 5.4 Signal processing for long-duration meteors -- 5.5 Meteor radar temperature estimates -- References -- Appendices.en_US
dc.language.isoen_USen_US
dc.subjectmeteor trailsen_US
dc.subjectmeteorsen_US
dc.subjectatmospheric temperatureen_US
dc.subjectbackscatteringen_US
dc.titleClassification and signal processing of radio backscatter from meteorsen_US
dc.typeThesisen_US
dc.type.degreemsen_US
dc.identifier.departmentDepartment of Electrical and Computer Engineeringen_US
dc.contributor.chairThorsen, Denise
dc.contributor.committeeBossert, Katrina
dc.contributor.committeeCollins, Richard
dc.contributor.committeeMayer, Charlie
refterms.dateFOA2020-02-13T21:06:35Z


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