DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, Hyungtae | ko |
dc.contributor.author | Ryu, Hanseok | ko |
dc.contributor.author | Rhudy, Matthew B. | ko |
dc.contributor.author | Lee, Dongjin | ko |
dc.contributor.author | Jang, Dongjin | ko |
dc.contributor.author | Lee, Changho | ko |
dc.contributor.author | Park, Youngmin | ko |
dc.contributor.author | Youn, Wonkeun | ko |
dc.contributor.author | Myung, Hyun | ko |
dc.date.accessioned | 2021-11-02T06:40:38Z | - |
dc.date.available | 2021-11-02T06:40:38Z | - |
dc.date.created | 2021-11-02 | - |
dc.date.created | 2021-11-02 | - |
dc.date.created | 2021-11-02 | - |
dc.date.created | 2021-11-02 | - |
dc.date.created | 2021-11-02 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | IEEE ROBOTICS AND AUTOMATION LETTERS, v.7, no.1, pp.17 - 24 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288498 | - |
dc.description.abstract | A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-the-art methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network | - |
dc.type | Article | - |
dc.identifier.wosid | 000707440500001 | - |
dc.identifier.scopusid | 2-s2.0-85116924849 | - |
dc.type.rims | ART | - |
dc.citation.volume | 7 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 17 | - |
dc.citation.endingpage | 24 | - |
dc.citation.publicationname | IEEE ROBOTICS AND AUTOMATION LETTERS | - |
dc.identifier.doi | 10.1109/LRA.2021.3117021 | - |
dc.contributor.localauthor | Myung, Hyun | - |
dc.contributor.nonIdAuthor | Ryu, Hanseok | - |
dc.contributor.nonIdAuthor | Rhudy, Matthew B. | - |
dc.contributor.nonIdAuthor | Lee, Dongjin | - |
dc.contributor.nonIdAuthor | Jang, Dongjin | - |
dc.contributor.nonIdAuthor | Lee, Changho | - |
dc.contributor.nonIdAuthor | Park, Youngmin | - |
dc.contributor.nonIdAuthor | Youn, Wonkeun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Redundancy | - |
dc.subject.keywordAuthor | Atmospheric modeling | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Aircraft | - |
dc.subject.keywordAuthor | Global Positioning System | - |
dc.subject.keywordAuthor | Unmanned aerial vehicles | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Aerial Systems | - |
dc.subject.keywordAuthor | applications | - |
dc.subject.keywordAuthor | sensor fusion | - |
dc.subject.keywordAuthor | field robots | - |
dc.subject.keywordAuthor | temporal convolutional network | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | unmanned aerial vehicles | - |
dc.subject.keywordAuthor | analytical redundancy | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | FLIGHT | - |
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