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GPS-Denied Navigation Using SAR Images and Neural Networks

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Citation Author(s):
Teresa White, Jesse Wheeler, Colton Lindstrom, Randall Christensen, Kevin Moon
Submitted by:
Teresa White
Last updated:
28 June 2021 - 5:40pm
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Teresa White
Paper Code:
IVMSP-31.5

Abstract 

Abstract: 

Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori. The distortions relative to the reference image are learned and exploited with a convolutional neural network to recover the initial navigational errors, which can be used to recover the true flight trajectory throughout the synthetic aperture. The proposed neural network approach is able to learn to predict the initial errors on both simulated and real SAR image data.

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Poster for the ICASSP 2021: GPS-Denied Navigation Using SAR Images and Neural Networks

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