ArticlePloS one2025
Design and comparative analysis of laser-based array systems for UAV detection in surveillance zones.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Identifying unmanned aerial vehicles (UAVs) is critical to protecting vital locations and infrastructures from potential attacks. The literature suggests a variety of detection methods, including conventional radar systems, acoustic detection, radio frequency signal detection, LiDAR, and camera-based techniques. LiDAR systems, in particular, offer high-resolution 3D mapping and precise distance measurements, which prove to be highly effective for detecting and tracking UAVs under various environmental conditions. This study presents two innovative LiDAR systems for UAV detection: a multi-array static LiDAR system and a one-array rotating LiDAR system. The multi-array static LiDAR employs arrays of laser light sources and concentrators arranged along a spherical shape. A central photodiode receives the transmittance of the reflected optical energy captured by the concentrators, enabling the precise identification of UAVs. The system's design focuses on achieving continuous, high-resolution coverage with minimal delay, making it suitable for monitoring wide areas. In contrast, the one-array rotating LiDAR utilizes a single array with rotational motion to scan the surveillance area. This approach prioritizes compactness and energy efficiency, which makes it advantageous for cost-sensitive applications. However, the rotational mechanism introduces trade-offs, such as increased mechanical wear and scanning latency. By conducting a comprehensive analysis of the design characteristics, this study evaluates the practicability and efficiency of these LiDAR solutions. Parameters such as the dimensions of the monitored region, sensor characteristics, component arrangement, and interspacing are considered to assess the effectiveness of both systems in identifying potential UAV threats.
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