Laser & Optoelectronics Progress, Volume. 61, Issue 20, 2011004(2024)

Advances in Long-Range Low-Slow-Small Target Detection Technology (Invited)

Chenyu Xu1,2, Jie Cao1,2、*, Feng Yang3, Jianbo Gao3, Li Zhang1, Yuyong Cui3, and Qun Hao4
Author Affiliations
  • 1School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081,China
  • 2Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314003, Zhejiang , China
  • 3Key Laboratory of Lidar and Device, Southwest Institute of Physics, Chengdu 610041, Sichuan , China
  • 4School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, Jilin , China
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    Figures & Tables(12)
    Multi-rotor UAV and fixed-wing UAV
    Schematic diagram of signals or images of typical UAV detection methods. (a) Sound wave signal; (b) radar signal; (c) radio signal; (d) visible light image; (e) infrared image; (f) lidar image
    Block diagram of remote UAV surveillance video detection algorithm[34]
    A drone detection system incorporating an infrared camera[40]. (a) Hardware system; (b) software block diagram; (c) detection effect (with visible light detection effect on the top and infrared detection effect on the bottom)
    3D laser radar detection UAV system[44]. (a) UAV detection environment (distance is about 50 m); (b) LiDAR scanning map and detection results; (c) enlarged view for UAV
    Schematic of the basic principle of single-photon lidar
    Representative achievements in the development process of single-photon lidar
    201.5 km imaging lidar system structure diagram and renderings[60]. (a) Visible light schematic diagram of the detection mountain; (b) schematic diagram of the experimental device; (c) equipment hardware photo; (d) view of the temporary laboratory at an altitude of 1770 m; (e) satellite map showing the starting and detection positions; (f) enlarged view of the snow mountain; (g) three-dimensional reconstruction outline map; (h) three-dimensional reconstruction profile map
    Three-dimensional imaging image of wind turbine under 10.5 km thick fog state. (a) Experimental detection satellite image; (b) three-dimensional reconstruction image of wind turbine; (c) three-dimensional reconstruction profile image; (d) experimental setup image; (e) visible light target real shot image
    • Table 1. Comparison of various drone detection methods

      View table

      Table 1. Comparison of various drone detection methods

      Detection methodDetection range /kmAdvantageDisadvantage
      Acoustic wave~0.2Does not require illumination, suitable for all-weather detection; strong penetration ability, unaffected by various fog, rain, and snow weather; low costLimited detection range; difficult to use in noisy environments; low resolution, susceptible to interference from target overlap
      Radar1‒8Effective detection range is relatively long; capable of all-weather detection; not affected by the type of drone; can achieve target classification based on micro-Doppler characteristicsSeverely affected by low-altitude/ultra-low-altitude clutter; limited resolution, difficult to distinguish drones from other small targets, leading to a high false alarm rate; challenging to detect drones with small radar cross-section and hovering drones
      Radio frequency1‒2Suitable for various types of drones and can detect drone operatorsLimited detection range; drones that do not emit radio signals, such as those hovering or operating autonomously, are difficult to detect
      Visible light~0.8High resolution, provides clear texture information; color information, which helps to distinguish targets from the surrounding environment; enables real-time, visual monitoring; strong target classification ability; low costClose detection range; limited by weather conditions; restricted in night or low-light environments; can be completely obscured by obstructions; does not have a ranging function
      Infrared~4Does not require illumination, suitable for all-weather detection; capable of real-time, visual monitoring; strong target classification abilityLimited detection range; affected by weather conditions; has lower resolution compared to visible light; does not have a ranging function. Thermal interference, changes in ambient temperature may cause false alarms
      Lidar~1Capable of all-weather detection; can perform 3D imaging, has a ranging function; not affected by electronic countermeasures and electromagnetic interferenceLimited detection range; high cost; performance is severely affected by rain, snow, fog, and other atmospheric conditions; high data volume, complex data processing
    • Table 2. Comparison of traditional lidar and single-photon lidar

      View table

      Table 2. Comparison of traditional lidar and single-photon lidar

      Performance metricTraditional lidarSingle-photon lidar
      Accuracy~10 cm~1 mm
      Sensitivity~109 photons~1 photon
      Laser power~100 mW~5 mW
      Pulse energy~10 mJ~10 μJ
      Imaging rangeHundreds of meters to kilometersHundreds of kilometers
    • Table 3. Detection probability and false alarm rate of single-photon lidar system at different distances and field of views

      View table

      Table 3. Detection probability and false alarm rate of single-photon lidar system at different distances and field of views

      FOV /mrad

      Measured

      distance /m

      SNR

      Detection

      probability /%

      False alarm

      rate /%

      3288.7592799.990.327
      126.5662099.990.301
      160.0011699.900.380
      16072.4732999.990.299
      80.5272599.990.309
      122.3261298.720.352
      22063.3963399.990.283
      76.4121899.970.301
      109.352993.920.366
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    Chenyu Xu, Jie Cao, Feng Yang, Jianbo Gao, Li Zhang, Yuyong Cui, Qun Hao. Advances in Long-Range Low-Slow-Small Target Detection Technology (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(20): 2011004

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    Paper Information

    Category: Imaging Systems

    Received: Apr. 22, 2024

    Accepted: Jul. 29, 2024

    Published Online: Nov. 5, 2024

    The Author Email: Cao Jie (caojie@bit.edu.cn)

    DOI:10.3788/LOP241119

    CSTR:32186.14.LOP241119

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