US 2023/0140890 A1 · Machine-Learning-Based Super Resolution of Radar Data

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.

Claims20

Dependent claims

A method comprising:

obtaining, from an electromagnetic sensor, sensor data;
generating, based on the sensor data, a first sensor image representing the sensor data in multiple dimensions;
generating, based on the sensor data, a second sensor image having a higher resolution in at least one of the multiple dimensions than the first sensor image;
training, by machine learning and based on the first sensor image being used as input data and the second sensor image being used as ground truth data, a model to generate a high-resolution sensor image that approximates the second sensor image, the generated high-resolution sensor image capable of being used for at least one of detecting objects, tracking objects, classification, or segmentation.

1 more element

A system comprising:

a radar sensor;
a trained model trained to generate a high-resolution sensor image;
at least one processor configured to:
receive, from the radar sensor, radar data;
determine, based on the radar data, a set of beam vectors derived from the radar data that encompasses all range bins and Doppler bins associated with the sensor data;
select a subset of the beam vectors, including a set of Doppler bins at all range bins, from the beam vectors that encompass all the range bins and Doppler bins;
apply a Fast Fourier Transform (FFT) to the subset of beam vectors;
input, into the trained model, magnitudes of the subset of beam vectors into at least a first channel of the model;
input, into the trained model, phases of the subset of beam vectors into at least a second channel of the model;
receive, from the trained model, the generated high-resolution sensor image;
and
output the generated high-resolution sensor image for at least one of detecting objects, tracking objects, classification, or segmentation.

9 more elements

Dependencies are inferred from the claim text.

Machine-Learning-Based Super Resolution of Radar Data

Life of this patent

Machine-Learning-Based Super Resolution of Radar Data

Life of this patent

Abstract

Claims20