Gps-Denied Navigation Using Sar Images And Neural Networks
| Autor: | Kevin R. Moon, Teresa White, Randall Christensen, Colton Lindstrom, Jesse Wheeler |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Synthetic aperture radar Artificial neural network business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Electrical Engineering and Systems Science - Image and Video Processing GPS signals Convolutional neural network Computer Science::Robotics FOS: Electrical engineering electronic engineering information engineering Trajectory Global Positioning System A priori and a posteriori Computer vision Artificial intelligence Sensitivity (control systems) business |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp39728.2021.9414421 |
| Popis: | 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. Comment: 5 pages, 5 figures |
| Databáze: | OpenAIRE |
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