Super-Resolution Radar Imaging With Sparse Arrays Using a Deep Neural Network Trained With Enhanced Virtual Data

Super-Resolution Radar Imaging With Sparse Arrays Using a Deep Neural Network Trained With Enhanced Virtual Data

This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and super-resolution. The results are validated by measuring the det...

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Bibliographic Details
Main Authors: Christian Schuessler, Marcel Hoffmann, Martin Vossiek
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Microwaves
Subjects:
Neural networks
super-resolution
sparse antenna arrays
compressed sensing
self-supervised learning
radar simulation
Online Access:https://ieeexplore.ieee.org/document/10175032/

Internet

https://ieeexplore.ieee.org/document/10175032/

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