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:
Jan. 14, 2025

Filed:

Apr. 26, 2021
Applicant:

Honeywell International Inc., Charlotte, NC (US);

Inventors:

Alberto Speranzon, Maple Grove, MN (US);

Andrew Stewart, Chanhassen, MN (US);

Shashank Shivkumar, St. Louis Park, MN (US);

Assignee:

Honeywell International Inc., Charlotte, NC (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G01C 19/5776 (2012.01); G01C 21/16 (2006.01); G06N 3/0499 (2023.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G01C 19/5776 (2013.01); G01C 21/16 (2013.01); G06N 3/0499 (2023.01);
Abstract

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.


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