LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques
As unmanned aerial vehicles (UAVs) are increasingly employed across various industries, the demand for robust and accurate detection has become crucial. Light detection and ranging (LiDAR) has developed as a vital sensor technology due to its ability to provide rich 3D spatial information, particula...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2757 |
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| author | Ulzhalgas Seidaliyeva Lyazzat Ilipbayeva Dana Utebayeva Nurzhigit Smailov Eric T. Matson Yerlan Tashtay Mukhit Turumbetov Akezhan Sabibolda |
| author_facet | Ulzhalgas Seidaliyeva Lyazzat Ilipbayeva Dana Utebayeva Nurzhigit Smailov Eric T. Matson Yerlan Tashtay Mukhit Turumbetov Akezhan Sabibolda |
| author_sort | Ulzhalgas Seidaliyeva |
| collection | DOAJ |
| description | As unmanned aerial vehicles (UAVs) are increasingly employed across various industries, the demand for robust and accurate detection has become crucial. Light detection and ranging (LiDAR) has developed as a vital sensor technology due to its ability to provide rich 3D spatial information, particularly in applications such as security and airspace monitoring. This review systematically explores recent innovations in LiDAR-based drone detection, deeply focusing on the principles and components of LiDAR sensors, their classifications based on different parameters and scanning mechanisms, and the approaches for processing LiDAR data. The review briefly compares recent research works in LiDAR-based only and its fusion with other sensor modalities, the real-world applications of LiDAR with deep learning, as well as the major challenges in sensor fusion-based UAV detection. |
| format | Article |
| id | doaj-art-5de71bb2276a42478d1c120acf87207b |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5de71bb2276a42478d1c120acf87207b2025-08-20T02:58:44ZengMDPI AGSensors1424-82202025-04-01259275710.3390/s25092757LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification TechniquesUlzhalgas Seidaliyeva0Lyazzat Ilipbayeva1Dana Utebayeva2Nurzhigit Smailov3Eric T. Matson4Yerlan Tashtay5Mukhit Turumbetov6Akezhan Sabibolda7Metropolitan College (MET), Boston University, Boston, MA 02215, USADepartment of Radio Engineering, Electronics and Telecommunications, International IT University, Almaty 050040, KazakhstanDepartment of Computer Science, SDU University, Almaty 040900, KazakhstanDepartment of Electronics, Telecommunications and Space Technologies, Satbayev University, Almaty 050013, KazakhstanDepartment of Computer and Information Technology, Purdue University, West Lafayette, IN 47907-2021, USADepartment of Electronics, Telecommunications and Space Technologies, Satbayev University, Almaty 050013, KazakhstanDepartment of Electronics, Telecommunications and Space Technologies, Satbayev University, Almaty 050013, KazakhstanDepartment of Electronics, Telecommunications and Space Technologies, Satbayev University, Almaty 050013, KazakhstanAs unmanned aerial vehicles (UAVs) are increasingly employed across various industries, the demand for robust and accurate detection has become crucial. Light detection and ranging (LiDAR) has developed as a vital sensor technology due to its ability to provide rich 3D spatial information, particularly in applications such as security and airspace monitoring. This review systematically explores recent innovations in LiDAR-based drone detection, deeply focusing on the principles and components of LiDAR sensors, their classifications based on different parameters and scanning mechanisms, and the approaches for processing LiDAR data. The review briefly compares recent research works in LiDAR-based only and its fusion with other sensor modalities, the real-world applications of LiDAR with deep learning, as well as the major challenges in sensor fusion-based UAV detection.https://www.mdpi.com/1424-8220/25/9/2757drone detectionunmanned aerial vehicles (UAVs)UAV detectionobject detectionLiDARLiDAR classifications |
| spellingShingle | Ulzhalgas Seidaliyeva Lyazzat Ilipbayeva Dana Utebayeva Nurzhigit Smailov Eric T. Matson Yerlan Tashtay Mukhit Turumbetov Akezhan Sabibolda LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques Sensors drone detection unmanned aerial vehicles (UAVs) UAV detection object detection LiDAR LiDAR classifications |
| title | LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques |
| title_full | LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques |
| title_fullStr | LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques |
| title_full_unstemmed | LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques |
| title_short | LiDAR Technology for UAV Detection: From Fundamentals and Operational Principles to Advanced Detection and Classification Techniques |
| title_sort | lidar technology for uav detection from fundamentals and operational principles to advanced detection and classification techniques |
| topic | drone detection unmanned aerial vehicles (UAVs) UAV detection object detection LiDAR LiDAR classifications |
| url | https://www.mdpi.com/1424-8220/25/9/2757 |
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