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

Wonkeun Youn, Hyungtae Lim, Hyoung Sik Choi, Matthew B. Rhudy, Hyeok Ryu, Sungyug Kim, Hyun Myung

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Article number9408352
Pages (from-to)5276-5283
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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