Deep learning for super resolution in a radar system
US10976412B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 1, 2019 |
| Grant date | Apr 13, 2021 |
| Priority date | — |
| Expiry date | Dec 7, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01S13/931
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A system and method to use deep learning for super resolution in a radar system include obtaining first-resolution time samples from reflections based on transmissions by a first-resolution radar system of multiple frequency-modulated signals. The first-resolution radar system includes multiple transmit elements and multiple receive elements. The method also includes reducing resolution of the first-resolution time samples to obtain second-resolution time samples, implementing a matched filter on the first-resolution time samples to obtain a first-resolution data cube and on the second-resolution time samples to obtain a second-resolution data cube, processing the second-resolution data cube with a neural network to obtain a third-resolution data cube, and training the neural network based on a first loss obtained by comparing the first-resolution data cube with the third-resolution data cube. The neural network is used with a second-resolution radar system to detect one or more objects.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.