- Forfatter
- Akhtar, Jabran
- Publisert
- 2020-06-11
- Emneord
- Radar
- Deteksjon
- Maskinlæring
- Permalenke
- http://hdl.handle.net/20.500.12242/2830
- DOI
- 10.1109/RADAR42522.2020.9114808
- Samling
- Articles
- Description
- Akhtar, Jabran. Sparse range-Doppler image construction with neural networks. IEEE International Conference on Radar 2020
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- 1813091.pdf
- Size: 2M
- Sammendrag
- The principles outlined by compressed sensing can permit a sensor to collect reduced amount of data and still reconstruct an exact outcome. This can for example be used to generate super-resolution sparse range-Doppler radar images while emitting a reduced number of pulses within a coherent processing interval. In this paper, we investigate the use of neural networks as a mean to solve the sparse reconstruction problem with specific emphasis towards range-Doppler images. The neural networks are trained to generate a sparse Doppler profile from incomplete time domain data in line with traditional sparse L1- norm minimization. We show that this approach is viable through fully connected feed forwarding networks and the results closely mimic sparse recovered range-Doppler maps.