Retrospective Cost Attitude Filtering with Noisy Measurements and Unknown Gyro Bias

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2401.13092.pdf (450.28 KB)

Links to Files

https://arxiv.org/abs/2401.13092

Permanent Link

https://doi.org/10.48550/arXiv.2401.13092
 http://hdl.handle.net/11603/31702

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UMBC Mechanical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-9326-0319
 https://orcid.org/0000-0002-4146-6275

Date

2024-01-23

Type of Work

journal articles
preprints
Text

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Citation of Original Publication

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
CC BY 4.0 DEED Attribution 4.0 International

Subjects

Abstract

Attitude filtering is a critical technology with applications in diverse domains such as aerospace engineering, robotics, computer vision, and augmented reality. Although attitude filtering is a particular case of the state estimation problem, attitude filtering is uniquely challenging due to the special geometric structure of the attitude parameterization. This paper presents a novel data-driven attitude filter, called the retrospective cost attitude filter (RCAF), for the SO(3) attitude representation. Like the multiplicative extended Kalman filter, RCAF uses a multiplicative correction signal, but instead of computing correction gains using Jacobians, RCAF computes the corrective signal using retrospective cost optimization and measured data. The RCAF filter is validated numerically in a scenario with noisy attitude measurements and noisy and biased rate-gyro measurements.