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V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System.

Byeong Hak Kim1,2, Danish Khan3, Ciril Bohak4

  • 1School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea. wonju09.choi@hanwha.com.

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Summary
This summary is machine-generated.

This study introduces a new 3D LADAR sensor detection method for small drones up to 2 km. The approach uses data augmentation and a novel algorithm, achieving high detection accuracy for drone surveillance.

Keywords:
3D LADAR3D sensorLiDARclusteringdrone detectionfusion data

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Area of Science:

  • Aerospace Engineering
  • Sensor Technology
  • Robotics

Background:

  • Electro-optical infrared (EO/IR) systems struggle with drone detection in complex environments.
  • Existing 3D LiDAR sensors lack the range and resolution for detecting small, distant drones.
  • Limitations of current 2D and 3D sensors necessitate advanced detection methodologies.

Purpose of the Study:

  • To develop and validate a detection methodology for a new 3D LADAR sensor.
  • To enable the detection of small drones at distances up to 2 kilometers.
  • To address the limitations of current drone detection technologies.

Main Methods:

  • Proposed a data augmentation method simulating laser beam and scanning characteristics for virtual targets.
  • Developed a drone detection algorithm employing voxel-based background subtraction.
  • Utilized the variable radially bounded nearest neighbor (V-RBNN) method for enhanced detection.

Main Results:

  • Achieved a 0.2 m L2 distance and 60% expected average overlap (EAO) index.
  • Satisfied specifications for detecting small drones (0.3 m size) at long ranges.
  • Demonstrated the effectiveness of the proposed data augmentation and detection algorithm.

Conclusions:

  • The developed 3D LADAR detection methodology is effective for identifying small drones at extended distances.
  • Data augmentation and the V-RBNN algorithm significantly improve detection performance.
  • This research advances counter-drone technology by enhancing sensor capabilities and detection algorithms.