Date of Award:
5-2013
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Electrical and Computer Engineering
Committee Chair(s)
Jacob H. Gunther
Committee
Jacob H. Gunther
Committee
Todd Moon
Committee
Don Cripps
Abstract
Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. Inverting this relationship, however, would allow us to estimate atmospheric parameters by taking hyperspectral measurements of the light emitted from the atmosphere. The particle filter is a method whereby one can estimate a hidden system state based on measurements, without ever having to directly invert the measurement relationship.
Traditionally, particle filters do not perform well in high-dimensional systems. This thesis presents a modification to the particle filter algorithm which can significantly improve performance of atmospheric parameter estimation as well as other high-dimensional estimation problems.
Checksum
e02c1cbb6b65fac7bc44f707ade9162c
Recommended Citation
Rawlings, Dustin, "Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters" (2013). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 1533.
https://digitalcommons.usu.edu/etd/1533
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