CARG AI4UAV: Counter UAS Simulator
The project aims to develop the following capabilities:
- Uncertainty in object detection, localization and payload classification
- Sensor fusion and multimodal learning
- Intruder behavior
- Simulation and training algorithms for effectively approaching and chasing the intruder UAS swarm
- Simulator modules design
- Analysis for different use cases, configurations, and environments
Research Papers
- N. Bowness, UAV Object Tracking with Modular Architecture, University of Ottawa, 2024.
- - Description: Signal processing and machine learning for fusion of airborne camera and radar data.
- H. Azad, V. Mehta, F. Dadboud, M. Bolic and I. Mantegh, Air-to-Air Simulated Drone Dataset for AI-powered problems, 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), Barcelona, Spain, 2023, pp. 1-7, doi: 10.1109/DASC58513.2023.10311339.
- - Description: This paper presents a comprehensive multi-view air-to-air simulated vision drone dataset.
- H. Azad, V. Mehta, M. Bolic and I. Mantegh, Simulated Dataset for the Loaded vs. Unloaded UAV Classification Problem Using Deep Learning, 2023 IEEE Sensors Applications Symposium (SAS), Ottawa, ON, Canada, 2023, pp. 1-6, doi: 10.1109/SAS58821.2023.10254046.
- - Description: This paper introduces the first published vision dataset for the loaded vs. unloaded UAV classification problem.
- X. Zhang, V. Mehta, M. Bolic and I. Mantegh, Hybrid AI-enabled Method for UAS and Bird Detection and Classification, IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020.
- - Description: A novel approach combining IMM filters and LSTM-based RNNs achieved 99.3% accuracy in classifying drones versus birds using synthetic trajectory data.
