The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 12, 2024

Filed:

Nov. 27, 2019
Applicant:

Qualcomm Incorporated, San Diego, CA (US);

Inventors:

Daniel Hendricus Franciscus Fontijne, Haarlem, NL;

Amin Ansari, San Diego, CA (US);

Bence Major, Amsterdam, NL;

Ravi Teja Sukhavasi, La Jolla, CA (US);

Radhika Dilip Gowaikar, San Diego, CA (US);

Xinzhou Wu, San Diego, CA (US);

Sundar Subramanian, San Diego, CA (US);

Michael John Hamilton, San Diego, CA (US);

Assignee:

QUALCOMM Incorporated, San Diego, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01S 13/60 (2006.01); G01S 7/02 (2006.01); G01S 7/41 (2006.01); G01S 13/931 (2020.01); G01S 17/931 (2020.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/58 (2022.01); G06V 20/70 (2022.01); G01S 7/295 (2006.01); G01S 13/86 (2006.01); G01S 13/89 (2006.01); G01S 17/89 (2020.01); G06F 18/2413 (2023.01); G06F 18/25 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01);
U.S. Cl.
CPC ...
G01S 13/931 (2013.01); G01S 7/022 (2013.01); G01S 7/417 (2013.01); G01S 13/60 (2013.01); G01S 17/931 (2020.01); G06V 10/764 (2022.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/58 (2022.01); G06V 20/70 (2022.01); G01S 7/2955 (2013.01); G01S 13/865 (2013.01); G01S 13/867 (2013.01); G01S 13/89 (2013.01); G01S 17/89 (2013.01); G06F 18/24133 (2023.01); G06F 18/251 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01);
Abstract

Disclosed are techniques for employing deep learning to analyze radar signals. In an aspect, an on-board computer of a host vehicle receives, from a radar sensor of the vehicle, a plurality of radar frames, executes a neural network on a subset of the plurality of radar frames, and detects one or more objects in the subset of the plurality of radar frames based on execution of the neural network on the subset of the plurality of radar frames. Further, techniques for transforming polar coordinates to Cartesian coordinates in a neural network are disclosed. In an aspect, a neural network receives a plurality of radar frames in polar coordinate space, a polar-to-Cartesian transformation layer of the neural network transforms the plurality of radar frames to Cartesian coordinate space, and the neural network outputs the plurality of radar frames in the Cartesian coordinate space.


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