State Estimation for HALE UAVs with Deep-Learning-Aided Virtual AOA/SSA Sensors for Analytical Redundancy

High-altitudelong-endurance (HALE) unmanned aerial vehicles (UAVs) are employed in a variety of fields because of their ability to fly for a long time at high altitudes, even in the stratosphere. Two paramount concerns exist: enhancing their safety during long-term flight and reducing their weight as much as possible to increase their energy efficiency based on analytical redundancy approaches. In this letter, a novel deep-learning-aided navigation filter is proposed, which consists of two parts: an end-to-end mapping-based synthetic sensor measurement model that utilizes long short-term memory (LSTM) networks to estimate the angle of attack (AOA) and sideslip angle (SSA) and an unscented Kalman filter for state estimation. Our proposed method can not only reduce the weight of HALE UAVs but also ensure their safety by means of an analytical redundancy approach. In contrast to conventional approaches, our LSTM-based method achieves better estimation by virtue of its nonlinear mapping capability.

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Work Title State Estimation for HALE UAVs with Deep-Learning-Aided Virtual AOA/SSA Sensors for Analytical Redundancy
Open Access
  1. Wonkeun Youn
  2. Hyungtae Lim
  3. Hyoung Sik Choi
  4. Matthew B. Rhudy
  5. Hyeok Ryu
  6. Sungyug Kim
  7. Hyun Myung
License In Copyright (Rights Reserved)
Work Type Article
  1. IEEE Robotics and Automation Letters
Publication Date April 19, 2021
Publisher Identifier (DOI)
Deposited November 12, 2021




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Work History

Version 1

  • Created
  • Added Final_File_IEEE_RAL-1.pdf
  • Added Creator Wonkeun Youn
  • Added Creator Hyungtae Lim
  • Added Creator Hyoung Sik Choi
  • Added Creator Matthew B. Rhudy
  • Added Creator Hyeok Ryu
  • Added Creator Sungyug Kim
  • Added Creator Hyun Myung
  • Published
  • Updated
  • Updated
  • Updated