公开(公告)号：US20230140890A1

公开(公告)日：2023-05-11

申请号：US17661223

申请日：2022-04-28

Applicant: Aptiv Technologies Limited

Inventor： Kanishka Tyagi ,  Yihang Zhang ,  Kaveh Ahmadi ,  Shan Zhang ,  Narbik Manukian

IPC: G01S13/89 ,  G06T3/40

CPC classification number: G01S13/89 ,  G06T3/4053 ,  G06T3/4046

Abstract: This document describes techniques and systems for machine-learning-based super resolution of radar data. A low-resolution radar image can be used as input to train a model for super resolution of radar data. A higher-resolution radar image, generated by an effective, but costly in terms of computing resources, traditional super resolution method, and the higher-resolution image can serve as ground truth for training the model. The resulting trained model may generate a high-resolution sensor image that closely approximates the image generated by the traditional method. Because this trained model needs only to be executed in feed-forward mode in the inference stage, it may be suited for real-time applications. Additionally, if low-level radar data is used as input for training the model, the model may be trained with more comprehensive information than can be obtained in detection level radar data.