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:
Sep. 17, 2024

Filed:

Oct. 06, 2021
Applicant:

The Boeing Company, Chicago, IL (US);

Inventors:

David Payton, Calabasas, CA (US);

Soheil Kolouri, Agoura Hills, CA (US);

Kangyu Ni, Calabasas, CA (US);

Qin Jiang, Oak Park, CA (US);

Assignee:

The Boeing Company, Arlington, VA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01S 13/90 (2006.01); G01S 7/41 (2006.01); G01S 13/933 (2020.01);
U.S. Cl.
CPC ...
G01S 13/9027 (2019.05); G01S 7/417 (2013.01);
Abstract

A computing system including a processor configured to train a synthetic aperture radar (SAR) classifier neural network. The SAR classifier neural network is trained at least in part by, at a SAR encoder, receiving training SAR range profiles that are tagged with respective first training labels, and, at an image encoder, receiving training two-dimensional images that are tagged with respective second training labels. Training the SAR classifier neural network further includes, at a shared encoder, computing shared latent representations based on the SAR encoder outputs and the image encoder outputs, and, at a classifier, computing respective classification labels based on the shared latent representations. Training the SAR classifier neural network further includes computing a value of a loss function based on the plurality of first training labels, the plurality of second training labels, and the plurality of classification labels and performing backpropagation based on the value of the loss function.


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