Conference proceeding
Autonomous Detection and Disambiguation of Martian Ion Trails Using Geometric Signal Processing Techniques
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2019-, pp.2312-2316
05/2019
DOI: 10.1109/ICASSP.2019.8683101
Abstract
We propose a novel "big data" application of geometric feature extraction techniques to autonomously identify and track the temporal evolution of charged particle trails in the Martian ionosphere. Specifically, we propose a Radon-transform extension to the geometric distance transform to algorithmically isolate potentially overlapping trail features in energy spectrograms. Our methods seek to connect large- scale statistical analysis with individual case studies and thus provide the computational framework or connecting theoretical models with potential terabytes of remote sensing data. Based on individual ion populations as the basic unit of observation, we provide data-driven results of applying our method over representative energy spectrograms generated from the NASA Mars Atmosphere and Volatile Evolution (MAVEN) mission data from the Solar Wind Ion Analyzer (SWIA) instrument.
Details
- Title: Subtitle
- Autonomous Detection and Disambiguation of Martian Ion Trails Using Geometric Signal Processing Techniques
- Creators
- Qiutong Jin - University of IowaAnanya Sen Gupta - University of IowaMirela Kapo - University of IowaEmma Hawk - University of IowaJasper S Halekas - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2019-, pp.2312-2316
- Publisher
- IEEE
- DOI
- 10.1109/ICASSP.2019.8683101
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Language
- English
- Date published
- 05/2019
- Academic Unit
- Physics and Astronomy; Electrical and Computer Engineering
- Record Identifier
- 9984197106902771
Metrics
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