Abstract
In this study, the manufacturing process of multi-sensors and deep learning based pilot assistance system for manned/unmanned aircraft is described. It consists of a total of two parts, this Part 1 describes the development process and results of Software-in-the-loop Simulation (SILS) and Hardware-in-the-loop Simulation (HILS) used in the development process. Optical cameras, radio altimeters, GPS/INS, ADS-B, and Radar modeling were performed to define and use the Sense and Avoid (SAA) concept. The development of the deep learning-based algorithm and the algorithm verification process through the HILS system is described.
| Translated title of the contribution | Development of an Advanced Pilot Assistant System Based on Multiple Surveillance Sensor and Deep Learning for GA Class Aircraft PartⅠ Algorithm Development and Validation |
|---|---|
| Original language | Korean |
| Pages (from-to) | 323-331 |
| Number of pages | 9 |
| Journal | Journal of the Korean Society for Aeronautical and Space Sciences |
| Volume | 52 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Korean Society for Aeronautical and Space Sciences.
Keywords
- Collision Avoidance Reinforcement Learning
- Hardware in the loop Simulation(HILS)
- Process in the loop Simulation(PILS)
- Sense-and-Avoid(SAA)
- Software in the loop Simulation(SILS)