Description
This project is in collaboration with the National Research Council’s Aerospace Research Centre, and it focuses on improving an existing Matlab model and converting it into Python code. The tool, called DAAMSim, is a publicly available modeling and simulation framework, developed by the National Research Council of Canada (NRC), to support the determination of DAA system requirements, and evaluation of DAA system performance. The framework incorporates the functional components including sensor and avoid models. This software is designed to run Monte Carlo simulations to model collisions between two aircrafts, with one aircraft executing collision avoidance maneuvers based on the implemented avoidance algorithm. The ideal candidate will need proficiency in Python to develop the new software and familiarity with Matlab to understand the existing code. Additionally, the current software is not optimized and part of the project will involve improving the efficiency and user-friendliness. The software consists of several modules: simulation of drone/manned aircraft flight path, transponder and camera models, Kalman filter, conflict prediction and avoidance. Upon completion, the student’s software will be posted on the NRC GitHub, with the student recognized as a contributor and co-author on the NRC-maintained software repository. This project is co-supervised by Dr. Iryna Borschova at NRC (Adjunct Professor, SCE).