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

Publications

2026-10-01

Conferences

B. Xia, I. Mantegh and M. Bolic, “Spatiotemporal Barrier and Gap-Guided APF for Deadlock-Resilient UAV Collision Avoidance,” IEEE CCTA 2026.

Published in IEEE CCTA 2026

by B. Xia, I. Mantegh and M. Bolic

The paper presents an enhanced artificial potential field method for UAV collision avoidance that combines time-to-collision weighting, safety-barrier repulsion, and gap guidance to address moving threats and escape local minima.

2026-04-25

Conferences

TRUST: Trajectory-guided State-Space Temporal Test-Time Adaptation

Published in ICLR, Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop

by F. Dadboud, H. Azad, M. Bolic, I. Mantegh,

TRUST is a backpropagation-free Bayesian test-time adaptation method for vision-language video object detection that improves robustness to gradual temporal distribution shifts by combining a global cache and scale statistics with trajectory-guided instance-level state-space filtering.

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2025-04-14

Conferences

Synthesized Control for In-field UAV Moving Target Interception via Deep Reinforcement Learning and Fuzzy Logic

Published in ICUAS 2025

by Bingze Xia, Mohammad Ahsan Akhlaque, Iraj Mantegh, Miodrag Bolic, Wenfang Xie

This paper develops AI-based multi-UAV interception algorithm through simulation and validating the system through real-world drone tests in dynamic environments.

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2024-12-15

Reports

UAV Object Tracking with Modular Architecture

Published in University of Ottawa

by Nathaniel Bowness

Signal processing and machine learning for fusion of airborne camera and radar data.

2023-10-01

Conferences

Air-to-Air Simulated Drone Dataset for AI-powered problems

Published in 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)

by Azad, H, Mehta, V, Dadboud, F, Bolic, M, Mantegh, I

This paper presents a comprehensive multi-view air-to-air simulated vision drone dataset.

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2023-05-18

Conferences

Simulated Dataset for the Loaded vs. Unloaded UAV Classification Problem Using Deep Learning

Published in 2023 IEEE Sensors Applications Symposium (SAS)

by Azad, H, Mehta, V, Bolic, M, Mantegh, I

This paper introduces the first published vision dataset for the loaded vs. unloaded UAV classification problem.

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2020-10-01

Conferences

Hybrid AI-enabled Method for UAS and Bird Detection and Classification

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC)

by X. Zhang, V. Mehta, M. Bolic and I. Mantegh

A novel approach combining IMM filters and LSTM-based RNNs achieved 99.3% accuracy in classifying drones versus birds using synthetic trajectory data.