Lightweight 3D LiDAR-Based UAV Tracking: An Adaptive Extended Kalman Filtering Approach

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References (16)

[1]
Detection and Classification of Small-sized UAVs and Birds in Sparse LiDAR Point Cloud *
2024K. Balla, Anita Keszler et al.
[2]
Camera Lidar Fusion for Unmanned Aerial Vehicle Detection
2024Cosimo Leuci, Sedat Dogru et al.
[3]
A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods
2024Bingshu Wang, Qiang Li et al.
[4]
Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR
2024Sándor Gazdag, T. Möller et al.
[5]
3D Object Detection and Tracking Based on Lidar-Camera Fusion and IMM-UKF Algorithm Towards Highway Driving
2023Chang Nie, Zhiyang Ju et al.
[6]
Towards Robust Lidar-based 3D Detection and Tracking of UAVs
2023Tasnim Azad Abir, E. Kuantama et al.
[7]
UAV Tracking with Solid-State Lidars: Dynamic Multi-Frequency Scan Integration
2023Iacopo Catalano, Sier Ha et al.
[8]
UAV Tracking with Lidar as a Camera Sensor in GNSS-Denied Environments
2023Sier Ha, Xianjia Yu et al.
[9]
Drone Detection Using Sparse Lidar Measurements
2022Sedat Dogru, Lino Marques
[10]
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[11]
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[12]
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
2019Jan Razlaw, Jan Quenzel et al.
[13]
Lidar-based detection and tracking of small UAVs
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[14]
Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges
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[15]
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Founder's Pitch

"A lightweight LiDAR-based UAV tracking system that enhances accuracy and reliability for swarm aerial robotics."

UAV TrackingScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

2/4 signals

5

Series A Potential

1/4 signals

2.5

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