Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR
The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a p...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119634/ |
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| author | Sandor Gazdag Tom Moller Anita Keszler Andras L. Majdik |
| author_facet | Sandor Gazdag Tom Moller Anita Keszler Andras L. Majdik |
| author_sort | Sandor Gazdag |
| collection | DOAJ |
| description | The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A pan-tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The system’s tracking capabilities (both in coverage and distance), as well as its accuracy are validated and compared to State Of The Art (SOTA) models, demonstrating improved performance, particularly in terms of coverage and maximum tracking distance. Our approach achieved accuracy on par with the SOTA indoor method while increasing the maximum detection range by approximately <inline-formula> <tex-math notation="LaTeX">$85\;\%$ </tex-math></inline-formula> beyond the SOTA outdoor method to <inline-formula> <tex-math notation="LaTeX">$130\;m$ </tex-math></inline-formula>. Additionally, our method yields at least a twofold increase in track coverage and returned point counts. |
| format | Article |
| id | doaj-art-2c1bad3b4b604ec0b35f78dc9ff374d8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2c1bad3b4b604ec0b35f78dc9ff374d82025-08-20T03:07:11ZengIEEEIEEE Access2169-35362025-01-011314165114166310.1109/ACCESS.2025.359685711119634Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDARSandor Gazdag0https://orcid.org/0000-0002-1983-6460Tom Moller1Anita Keszler2Andras L. Majdik3https://orcid.org/0000-0003-1807-2865HUN-REN SZTAKI Institute for Computer Science and Control, Budapest, HungaryHUN-REN SZTAKI Institute for Computer Science and Control, Budapest, HungaryHUN-REN SZTAKI Institute for Computer Science and Control, Budapest, HungaryHUN-REN SZTAKI Institute for Computer Science and Control, Budapest, HungaryThe use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A pan-tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The system’s tracking capabilities (both in coverage and distance), as well as its accuracy are validated and compared to State Of The Art (SOTA) models, demonstrating improved performance, particularly in terms of coverage and maximum tracking distance. Our approach achieved accuracy on par with the SOTA indoor method while increasing the maximum detection range by approximately <inline-formula> <tex-math notation="LaTeX">$85\;\%$ </tex-math></inline-formula> beyond the SOTA outdoor method to <inline-formula> <tex-math notation="LaTeX">$130\;m$ </tex-math></inline-formula>. Additionally, our method yields at least a twofold increase in track coverage and returned point counts.https://ieeexplore.ieee.org/document/11119634/Dronestrackingtrajectory trackingparticle filterssensors |
| spellingShingle | Sandor Gazdag Tom Moller Anita Keszler Andras L. Majdik Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR IEEE Access Drones tracking trajectory tracking particle filters sensors |
| title | Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR |
| title_full | Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR |
| title_fullStr | Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR |
| title_full_unstemmed | Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR |
| title_short | Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR |
| title_sort | detection and tracking of mavs using a rosette scanning pattern lidar |
| topic | Drones tracking trajectory tracking particle filters sensors |
| url | https://ieeexplore.ieee.org/document/11119634/ |
| work_keys_str_mv | AT sandorgazdag detectionandtrackingofmavsusingarosettescanningpatternlidar AT tommoller detectionandtrackingofmavsusingarosettescanningpatternlidar AT anitakeszler detectionandtrackingofmavsusingarosettescanningpatternlidar AT andraslmajdik detectionandtrackingofmavsusingarosettescanningpatternlidar |