A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications

Area of Science:

• Robotics and Autonomous Systems
• Computer Vision
• Aerospace Engineering

Background:

• Autonomous navigation for Unmanned Aerial Vehicles (UAVs) is critical for advanced missions.
• Cooperative sensing enhances situational awareness and navigation capabilities.
• Robust target detection and tracking are essential for safe UAV operations, especially in challenging environments.

Purpose of the Study:

• To develop and validate a visual-based autonomous detection and tracking system for UAVs.
• To enable cooperative navigation using a monocular camera and inter-vehicle communication.
• To improve UAV navigation performance in both nominal and GPS-challenged scenarios.

Main Methods:

• Utilized template matching and morphological filtering for robust visual tracking.
• Integrated navigation hints (relative positioning, attitude) to optimize image processing.
• Leveraged a reliable inter-vehicle data link for cooperative information exchange.

Main Results:

• Demonstrated autonomous detection and tracking of cooperative vehicles across a wide range of distances (meters to tens of meters).
• Achieved robustness against variations in illumination, target scale, and background clutter.
• Showcased improved computational efficiency and a balanced trade-off between detection accuracy and false alarms.

Conclusions:

• The proposed visual-based approach is a foundational element for cooperative UAV architectures.
• This method significantly enhances UAV navigation, particularly in environments with limited or no Global Navigation Satellite System (GNSS) coverage.
• The system's performance was validated through experimental flight data, confirming its practical applicability.