TY - JOUR
T1 - Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy with a Temporal Convolutional Network
AU - Lim, Hyungtae
AU - Ryu, Hanseok
AU - Rhudy, Matthew B.
AU - Lee, Dongjin
AU - Jang, Dongjin
AU - Lee, Changho
AU - Park, Youngmin
AU - Youn, Wonkeun
AU - Myung, Hyun
N1 - Funding Information:
This letter was recommended for publication by Associate Editor P. Castillo and Editor P. Pounds upon evaluation of the reviewers' comments. This research was supported in part under Grant (21CAUV-B151724-03) from Development of Certification Technology for Small Unmanned Aerial Vehicle Systems' Program Through the Korea Agency for Infrastructure Technology Advancement (KAIA), in part by the Ministry of Land, Infrastructure and Transport (MOLIT), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1093445).
Publisher Copyright:
© 2016 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
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U2 - 10.1109/LRA.2021.3117021
DO - 10.1109/LRA.2021.3117021
M3 - Article
AN - SCOPUS:85116924849
VL - 7
SP - 17
EP - 24
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 1
ER -