Automated Air Traffic Control
for Non-Towered Airports

Supervised by Prof. Mykel Kochenderfer
Jun 01, 2016
Ph.D. thesis: applying Machine Learning tools to air traffic control
Automated Air Traffic Control<br>for Non-Towered Airports

For my Ph.D. thesis, I modeled the behavior of aircraft in the airport pattern as a hidden Markov Model (HMM) whose parameters are learned from real-world radar observations. Then I determined optimal advisories to reduce the risk of collision by formulating the problem as a partially observable semi-Markov decision process (POSMDP), and solved it using Reinforcement Learning techniques.

In order to address the computational complexity of solving the problem, I used different approximation methods including exponential sojourn times, phase-type distributions, online algorithms, and particle filters for belief estimation.

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