About Me

• Aerospace Engineer, Nearthlab, Daejeon, South Korea
  • Currently working on:
    1. Onboard UAV Navigation (항법)
       • Lightweight & simple AHRS, GNSS/INS fusion using EKF
       • Bare metal firmware on an embedded system (FC)
       • Flight test (1. FC development, 2. manual pilot, 3. remote commanding(GCS))
    2. Indoor Navigation (항행)
       • Mission-specific autonomous flight, such as inspection.
         • adapting contemporary LiDAR/Visual SLAM algorithms
       • SITL- or Drone(FC)-Companion computer(MMU) integrated development
• PhD in Aerospace Engineering, KAIST
  • Specialty**: State estimation in aerospace applications**
• Email: **[email protected]** (personal) / [email protected] (work)

Research Interests

<aside> 💡 : Establish the current status of aerial vehicles and decide the appropriate course of actions based on the findings

</aside>

• A—State estimation and inference for aerospace systems
  • 1—Bayesian estimation, particularly via particle filter (PF), and making it better in terms of robustness, efficiency, and accuracy
  • 2—Database-referenced navigation (DBRN) under GNSS-challenging situations
• B—Informative control and decision-making of aerospace systems (based on A)
  • 1—Active sensing
  • 2—Reinforcement learning applications to complex aerospace systems

Links

• Google Scholar🎓 **| Web of Science📔| Github personal, work👨🏻‍💻| LinkedIn💼| Web Page**🌐 (WIP) | CV📋(WIP) **| Personal Blog**✍🏻 (non-academic)

Publications

1. Contours-seeking Proposal Density Particle Filter and Resilient Terrain-referenced Navigation (early access)

   IEEE Transactions on Aerospace and Electronic Systems (TAES), 2025, Accepted for publication

   Park, J., and Bang, H.

2. Enhanced Terrain-Referenced Navigation through Adaptive Radar Altimeter Error Estimation with Monte Carlo Sampling

   International Journal of Aeronautical and Space Sciences (IJASS), 2025, vol. 26, no. 1, pp. 1954-1974

   Kim, S., Park, J., Bang, H.

3. Deep Reinforcement Learning-Based Attitude Control for Spacecraft Using Control Moment Gyros

   Advances in Space Research (AISR), 2025, vol. 75, no. 1, pp. 1129-1144

   Oghim, S., Park, J., Bang, H., and Leeghim, H.

4. Sparse Instantiation of Bias Nodes for Factor Graph-based Terrain-referenced Navigation

   International Journal of Control, Automation, and Systems (IJCAS), 2024, vol. 22, no. 11, pp. 3364-3376

   Park, J. and Bang, H.

5. Visual Semantic Context and Map-based Rotation Invariant Efficient Estimation of Position and Heading

   NAVIGATION: Journal of the Institute of the Navigation (ION), vol. 71, no. 1, p. 634, 2024

   Park, J., Kim, S., Hong, K., and Bang, H.

6. Data-driven Hazard Avoidance Landing of Parafoil: A Deep Reinforcement Learning Approach

   AIAA Journal of Aerospace Information Systems (JAIS), vol. 21, no. 1, pp. 58-74, 2024

   Park, J., and Bang, H.

7. Balanced Cooperative Target Search of Mobile Sensor Fleet under Localization Uncertainty

   2022 IEEE 61st Conference on Decision and Control (CDC) (pp. 6626-6631). IEEE.

   Park, J., Lee, H., and Bang, H.