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LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and

Ulzhalgas Seidaliyeva¹, Lyazzat Ilipbayeva², Dana Utebayeva³

¹Metropolitan College (MET), Boston University, Boston, MA 02215, USA.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This review explores Light Detection and Ranging (LiDAR) for detecting unmanned aerial vehicles (UAVs). It covers LiDAR principles, data processing, and its fusion with other sensors for enhanced drone detection and security.

Keywords:

3D object detection LiDAR LiDAR classifications UAV detection deep learning deep learning for point cloud processing drone detection object detection point clouds scanning mechanism unmanned aerial vehicles (UAVs)

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Area of Science:

Robotics and Automation
Sensor Technology
Artificial Intelligence

Background:

Unmanned Aerial Vehicles (UAVs) are increasingly used in various sectors, necessitating advanced detection methods.
Light Detection and Ranging (LiDAR) offers precise 3D spatial data, crucial for security and airspace monitoring.
Accurate drone detection is vital for safety and regulatory compliance.

Purpose of the Study:

To systematically review recent innovations in LiDAR-based detection of UAVs.
To detail LiDAR sensor principles, classifications, and data processing techniques.
To compare LiDAR-only and sensor fusion approaches for UAV detection.

Main Methods:

Systematic literature review of LiDAR-based drone detection technologies.
Analysis of LiDAR sensor principles, components, and scanning mechanisms.
Exploration of data processing algorithms and deep learning applications in LiDAR data.

Main Results:

LiDAR is a key technology for UAV detection due to its 3D sensing capabilities.
Fusion of LiDAR with other sensors shows promise for improved detection accuracy.
Deep learning enhances the analysis of LiDAR data for real-world applications.

Conclusions:

LiDAR technology is pivotal for robust UAV detection systems.
Sensor fusion strategies, particularly with deep learning, offer significant advancements in drone detection.
Addressing challenges in sensor fusion is critical for future development.