Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy with a Temporal Convolutional Network

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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Work Title Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy with a Temporal Convolutional Network
Access
Open Access
Creators
  1. Hyungtae Lim
  2. Hanseok Ryu
  3. Matthew B. Rhudy
  4. Dongjin Lee
  5. Dongjin Jang
  6. Changho Lee
  7. Youngmin Park
  8. Wonkeun Youn
  9. Hyun Myung
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. IEEE Robotics and Automation Letters
Publication Date October 1, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1109/LRA.2021.3117021
Deposited November 17, 2021

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Version 1
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  • Created
  • Added 21-1069_02_MS.PDF
  • Added Creator Hyungtae Lim
  • Added Creator Hanseok Ryu
  • Added Creator Matthew B. Rhudy
  • Added Creator Dongjin Lee
  • Added Creator Dongjin Jang
  • Added Creator Changho Lee
  • Added Creator Youngmin Park
  • Added Creator Wonkeun Youn
  • Added Creator Hyun Myung
  • Published
  • Updated
  • Updated
  • Updated