Project SCOUT: Interceptor Drone for Perimeter Defense

Summary

The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for perimeter defense.

Abstract Summary

The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for perimeter defense. This paper presents \textbf{SCOUT} (Spatial Computation for Optimized UAV Tracking), a ROS-integrated onboard perception and control framework for real-time aerial defense against incoming UAVs. SCOUT performs visual detection, target association, track filtering, and control command generation directly onboard the defender UAV, without relying on external sensing infrastructure or ground-station computation. To provide stable control inputs, the perception pipeline combines TensorRT-accelerated drone detection with ByteTrack-based association and a lightweight track-retention state machine. The state machine rejects abrupt target jumps and maintains short-term target continuity during temporary detection degradation, reducing unstable control responses caused by false detections or target switching. We evaluate the proposed architecture through an integrated hardware deployment executing a planar ``goalkeeping'' interception strategy. In this setting, the defender UAV tracks the incoming target and adjusts its motion to maintain a blocking configuration near the protected boundary. Real-world flight results show that SCOUT maintains valid target detections for 92.2\% of frames while operating at real-time onboard detection rates, demonstrating the feasibility of visual tracking and closed-loop control for UAV perimeter defense. A video demonstration of the end-to-end perimeter defense operation is available online. https://figshare.com/s/497befc033091fc0b84f

Key Points

The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for perimeter defense.
This paper presents \textbf{SCOUT} (Spatial Computation for Optimized UAV Tracking), a ROS-integrated onboard perception and control framework for real-time aerial defense against incoming UAVs.
SCOUT performs visual detection, target association, track filtering, and control command generation directly onboard the defender UAV, without relying on external sensing infrastructure or ground-station computation.
To provide stable control inputs, the perception pipeline combines TensorRT-accelerated drone detection with ByteTrack-based association and a lightweight track-retention state machine.
The state machine rejects abrupt target jumps and maintains short-term target continuity during temporary detection degradation, reducing unstable control responses caused by false detections or target switching.

Method

SCOUT is designed to convert onboard visual observations of an incoming UAV into stable control commands for real-time perimeter defense. The system follows a sequential perception-to-control approach, where a drone detector is first trained and fine-tuned for the target UAV, then deployed through a low-latency onboard inference pipeline, as shown in Fig. 1 .

The resulting detections are temporally associated and filtered to produce stable target-center errors, which are finally used by the interceptor controller to generate flight commands.

Method — Project SCOUT: Interceptor Drone for Perimeter Defense — Fig. 2: First-person view from the defender UAV. The detected target UAV is outlined in green, and the image-plane target-center offsets used for control are shown by the red and yellow markers.

Experimental Results

Real-world hardware experiments were designed to verify whether the SCOUT platform can provide real-time target-center estimates and support closed-loop perimeter-defense maneuvers without ground-station computation. We first describe the experimental UAV platforms and test environment, then report detection-stream performance from a representative flight sequence and discuss the observed system behavior.

For this experiment, we use the DJI Neo as the target/adversarial drone.

Experimental Results — Project SCOUT: Interceptor Drone for Perimeter Defense — Fig. 3: Invader Drone: DJI Neo (left) and Defender Drone: Custom Built (right)

Sources and Demonstrations

Figures are reproduced from Yousuf et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.