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公开(公告)号：US20230213936A1
公开(公告)日：2023-07-06
申请号：US17569316
申请日：2022-01-05
Applicant: Honeywell International Inc.
Inventor： Andrew Stewart , Christopher J. Mauer , Shashank Shivkumar , Thomas Jakel
CPC classification number: G05D1/0088 , G01P15/02 , G06N20/00
Abstract: 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.
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公开(公告)号：US20250020485A1
公开(公告)日：2025-01-16
申请号：US18352477
申请日：2023-07-14
Applicant: Honeywell International Inc.
Inventor： Christopher J. Mauer , Jens M. Henrickson , Patrick James Collins
Abstract: A method of dynamic, real-time generation of a blended output from a plurality of sensors is provided. The method includes, at a frame rate, periodically storing samples from the plurality of sensors; band pass filtering the stored samples separately for each of the plurality of sensors over a time scale characteristic of a type of error for the plurality of sensors; storing the filtered samples; at an accumulation rate, iteratively updating a covariance matrix based on a selected number of filtered samples, removing data from the covariance matrix for any of the plurality of sensors that have failed; and calculating, based on the covariance matrix, changes to real-time coefficients to be applied to the outputs of each sensor of the plurality of sensors; and at the frame rate, applying the changes to the real-time coefficients; and calculating the blended output for the plurality of sensors based on the real-time coefficients.
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公开(公告)号：US12189388B2
公开(公告)日：2025-01-07
申请号：US17569316
申请日：2022-01-05
Applicant: Honeywell International Inc.
Inventor： Andrew Stewart , Christopher J. Mauer , Shashank Shivkumar , Thomas Jakel
Abstract: 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.
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4.发明公开公开(公告)号：US20240302169A1
公开(公告)日：2024-09-12
申请号：US18179609
申请日：2023-03-07
Applicant: Honeywell International Inc.
Inventor： Christopher J. Mauer , Shuang Li , Zhizhen Shen , Jingwei Li
CPC classification number: G01C21/188 , B81B7/02 , G01C21/18 , B81B2201/0235 , B81B2201/0242 , B81B2207/05 , G01C21/166
Abstract: An inertial measurement unit (IMU). The IMU includes: a plurality of micro-electromechanical system (MEMS) sensors, each having an output; a memory for storing calibration coefficients separately for each of the plurality of MEMS sensors, blending weights for each of the plurality of MEMS sensors, and data blending instructions for blending the outputs of the plurality of MEMS sensors; and a processor, coupled to the memory and the plurality of MEMS sensors, configured to execute the data blending instructions to apply the calibration coefficients separately to each of the plurality of MEMS sensors and the blending weights to the outputs of the plurality of MEMS sensors to create a blended output for the IMU; wherein the blending weights are calculated based on a plurality of test parameters for the plurality of MEMS sensors using at least one of a harmonic and a geometric mean of the plurality of test parameters.
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