Deep learning for de-aliasing and configuring a radar system
US11009591B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 1, 2019 |
| Grant date | May 18, 2021 |
| Priority date | — |
| Expiry date | Dec 22, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01S13/931
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Deep learning in a radar system includes obtaining unaliased time samples from a first radar system. A method includes under-sampling the un-aliased time samples to obtain aliased time samples of a first configuration, matched filtering the un-aliased time samples to obtain an un-aliased data cube and the aliased time samples to obtain an aliased data cube, and using a first neural network to obtain a de-aliased data cube. A first neural network is trained to obtain a trained first neural network. The under-sampling of the un-aliased time samples is repeated to obtain second aliased time samples of a second configuration. The method includes training a second neural network to obtain a trained second neural network, comparing results to choose a selected neural network corresponding with a selected configuration, and using the selected neural network with a second radar system that has the selected configuration to detect one or more objects.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.