Invention Grant
- Patent Title: Deep learning for super resolution in a radar system
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Application No.: US16264807Application Date: 2019-02-01
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Publication No.: US10976412B2Publication Date: 2021-04-13
- Inventor: Yaron Eshet , Igal Bilik , Oded Bialer
- Applicant: GM Global Technology Operations LLC
- Applicant Address: US MI Detroit
- Assignee: GM Global Technology Operations LLC
- Current Assignee: GM Global Technology Operations LLC
- Current Assignee Address: US MI Detroit
- Agency: Cantor Colburn LLP
- Main IPC: G01S7/41
- IPC: G01S7/41 ; G01S13/931

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.
Public/Granted literature
- US20200249314A1 DEEP LEARNING FOR SUPER RESOLUTION IN A RADAR SYSTEM Public/Granted day:2020-08-06
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