Soft Error Assessment of Attitude Estimation Algorithms Running on Resource-Constrained Devices Under Neutron Radiation
There is a growing incorporation of unmanned aerial vehicles (UAVs) within remote and urban environments due to their versatility and ability to access hard-to-reach and/or congested places. UAVs offer low-cost solutions for many applications, including healthcare (e.g., medical supplies delivery) a...
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Published in | IEEE transactions on nuclear science Vol. 71; no. 8; pp. 1511 - 1519 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
Subjects | |
Online Access | Get full text |
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Summary: | There is a growing incorporation of unmanned aerial vehicles (UAVs) within remote and urban environments due to their versatility and ability to access hard-to-reach and/or congested places. UAVs offer low-cost solutions for many applications, including healthcare (e.g., medical supplies delivery) and surveillance during public events, protests, or emergencies (e.g., a nuclear accident). However, drone utilization in urban areas often relies on strict regulations to ensure safe and responsible operation. UAVs are subject to radiation-induced soft errors, and identifying the most vulnerable software and hardware components to radiation exposure is an advisable task, which is difficult to undertake. An essential task to UAVs correct operation is attitude estimation (AE). This article assesses the soft error reliability of three AE algorithms running on two resource-constrained microprocessors under neutron radiation. Results suggest that the extended Kalman filter (EKF) algorithm provides the best Mean Work to Failure (MWTF) result for critical fault events, which is about <inline-formula> <tex-math notation="LaTeX">3\times </tex-math></inline-formula> more than the indirect Kalman filter (IKF) and <inline-formula> <tex-math notation="LaTeX">1.5\times </tex-math></inline-formula> more with respect to the novel quaternion Kalman filter (NQKF) algorithm. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0018-9499 1558-1578 |
DOI: | 10.1109/TNS.2024.3378689 |