• Acta Optica Sinica
  • Vol. 41, Issue 19, 1928001 (2021)
Jian Li1、2、3, Kunpeng Wang4, Kai Jin1、2, Chen Xu1、2、3, Hanchu Fu1、2, and Kai Wei1、2、*
Author Affiliations
  • 1Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 2Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China
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    DOI: 10.3788/AOS202141.1928001 Cite this Article Set citation alerts
    Jian Li, Kunpeng Wang, Kai Jin, Chen Xu, Hanchu Fu, Kai Wei. Inverse Synthetic Aperture Lidar Motion Compensation Imaging Algorithm for Maneuvering Targets[J]. Acta Optica Sinica, 2021, 41(19): 1928001 Copy Citation Text show less
    Geometric model of the ISAL
    Fig. 1. Geometric model of the ISAL
    Flow chart of the NM-PSO imaging algorithm
    Fig. 2. Flow chart of the NM-PSO imaging algorithm
    Target model of the simulation experiment
    Fig. 3. Target model of the simulation experiment
    Distance image and two-dimensional imaging results obtained by different algorithms. (a) Uncompensation;(b) uncompensation-PSO; (c) cross-correlation method; (d) cross-correlation method and PSO; (e) NM simplex; (f) NM-PSO
    Fig. 4. Distance image and two-dimensional imaging results obtained by different algorithms. (a) Uncompensation;(b) uncompensation-PSO; (c) cross-correlation method; (d) cross-correlation method and PSO; (e) NM simplex; (f) NM-PSO
    Velocity estimation error
    Fig. 5. Velocity estimation error
    Parameter estimation results. (a) Parameter estimation error; (b) distribution of acceleration particles
    Fig. 6. Parameter estimation results. (a) Parameter estimation error; (b) distribution of acceleration particles
    Two-dimensional imaging results and entropy distribution. (a) PGA algorithm;(b) NM simplex method; (c) PSO algorithm
    Fig. 7. Two-dimensional imaging results and entropy distribution. (a) PGA algorithm;(b) NM simplex method; (c) PSO algorithm
    Experimental results of shot noise. (a) IE; (b) NM simplex method; (c) PSO algorithm
    Fig. 8. Experimental results of shot noise. (a) IE; (b) NM simplex method; (c) PSO algorithm
    Experimental results of phase noise. (a) IE; (b) NM simplex method; (c) PSO algorithm
    Fig. 9. Experimental results of phase noise. (a) IE; (b) NM simplex method; (c) PSO algorithm
    ParameterValue
    Signal bandwidth B /GHz4
    Carrier wavelength λc /nm1550
    Pulse repetition rate fPRF /kHz50
    Pulse width Tp /μs10
    Sampling rate fs /MHz80
    Initial distance R0 /km3
    Range cell M800
    Pulse number N128
    Table 1. System parameters
    Optimization algorithmPoint 1Point 2Point 3Point 4Point 5
    PGA1.23971.22601.02050.78911.0870
    NM simplex0.83580.57061.05161.08400.7979
    NM-PSO0.48970.20390.69990.99030.5421
    Table 2. IE of partial points
    Jian Li, Kunpeng Wang, Kai Jin, Chen Xu, Hanchu Fu, Kai Wei. Inverse Synthetic Aperture Lidar Motion Compensation Imaging Algorithm for Maneuvering Targets[J]. Acta Optica Sinica, 2021, 41(19): 1928001
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