Honeywell International Inc.

Thomas Jakel

Brian W Schipper

Xiao Cao Cao

Andrew Stewart

Shashank Shivkumar

Yanling Min

Christopher J Mauer

Xiao Cao

Systems and methods for multiple inertial measurement unit sensor fusion using machine learning are provided herein. In certain embodiments, a system includes inertial sensors that produce inertial measurements, a memory unit that stores a fusion model produced by at least one machine learning algorithm, and a processor that receives inertial measurements, where the processor applies the fusion model to the inertial measurements. The fusion model directs the processor to extract features from the inertial measurements, and to select inertial measurements based on a sensor in the plurality of inertial sensors that produced the inertial measurements. Also, the fusion model directs the processor to apply weights to the selected inertial measurements based on the extracted features, to apply compensation coefficients to the selected inertial measurements, and to fuse the selected inertial measurements into an inertial navigation solution.

Multiple inertial measurement unit sensor fusion using machine learning

Systems and methods for GNSS ambiguity resolution are described herein. In some examples, the systems and methods utilize multiple search engines in parallel to validate potential integer candidates for ambiguity resolution using adaptively adjusted residual thresholds.

Systems and methods for GNSS ambiguity resolution

Growing community of inventors

Forrest Lake, MN, United States of America

Thomas Jakel