Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Taylor Hostetter, Senior
Lead Presenter's Name
Taylor Hostetter
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Kranthi Kumar Deveerasetty
Abstract
Lightweight UAV-to-UAV Detection and Tracking for Advanced Air Mobility Applications addresses the significant challenge of reliable UAV-to-UAV detection on resource-constrained platforms, particularly within Advanced Air Mobility (AAM) environments where dense, low-altitude airspace requires robust detect-and-avoid capabilities. This work presents the development and experimental evaluation of a lightweight detection and tracking framework for autonomous detect-and-avoid applications. The approach is designed to support real-time onboard operation in multi-vehicle environments characteristic of emerging AAM systems. The proposed framework integrates optical and LiDAR sensing with a low-complexity machine learning decision-support layer that reduces false detections without replacing the underlying control-oriented detection pipeline. This design preserves deterministic behavior and computational efficiency, both of which are critical for scalable deployment in AAM platforms. Experimental validation includes both stationary and moving-frame testing under varying motion, background clutter, and environmental conditions, using quantitative metrics to evaluate detection accuracy and robustness. Preliminary results show that a preprocessing dehazing algorithm improves detection performance in low-visibility conditions by increasing the consistency of target identification. A YOLOv8-based detector, integrated alongside the primary detection pipeline, improves recall, particularly for stationary UAVs that are difficult to detect using motion-based methods alone. Most notably, a post-processing LSTM-based tracker significantly reduces false track persistence, resulting in the largest improvement in overall precision. This work demonstrates that machine learning can be selectively integrated to enhance system robustness while preserving the computational efficiency and deterministic behavior required for real-time control. These characteristics are essential for enabling reliable multi-UAV sensing, closed-loop collision avoidance, and scalable airspace integration in Advanced Air Mobility systems.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Artificial Intelligence and Robotics Commons, Navigation, Guidance, Control and Dynamics Commons, Systems and Communications Commons
Lightweight UAV-to-UAV Detection and Tracking for Advanced Air Mobility Applications
Lightweight UAV-to-UAV Detection and Tracking for Advanced Air Mobility Applications addresses the significant challenge of reliable UAV-to-UAV detection on resource-constrained platforms, particularly within Advanced Air Mobility (AAM) environments where dense, low-altitude airspace requires robust detect-and-avoid capabilities. This work presents the development and experimental evaluation of a lightweight detection and tracking framework for autonomous detect-and-avoid applications. The approach is designed to support real-time onboard operation in multi-vehicle environments characteristic of emerging AAM systems. The proposed framework integrates optical and LiDAR sensing with a low-complexity machine learning decision-support layer that reduces false detections without replacing the underlying control-oriented detection pipeline. This design preserves deterministic behavior and computational efficiency, both of which are critical for scalable deployment in AAM platforms. Experimental validation includes both stationary and moving-frame testing under varying motion, background clutter, and environmental conditions, using quantitative metrics to evaluate detection accuracy and robustness. Preliminary results show that a preprocessing dehazing algorithm improves detection performance in low-visibility conditions by increasing the consistency of target identification. A YOLOv8-based detector, integrated alongside the primary detection pipeline, improves recall, particularly for stationary UAVs that are difficult to detect using motion-based methods alone. Most notably, a post-processing LSTM-based tracker significantly reduces false track persistence, resulting in the largest improvement in overall precision. This work demonstrates that machine learning can be selectively integrated to enhance system robustness while preserving the computational efficiency and deterministic behavior required for real-time control. These characteristics are essential for enabling reliable multi-UAV sensing, closed-loop collision avoidance, and scalable airspace integration in Advanced Air Mobility systems.