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Volume 36 Issue 1
Dec.  2011
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Algorithm and simulation of downward velocity azimuth display of airborne wind lidars

  • Received Date: 2011-05-12
    Accepted Date: 2011-06-20
  • In order to study how to detect three-dimension wind field under an airplane,a model for wind field was built based on ground-based wind lidars,and the radial velocity was optimized by means of Levenber-Marquardt(LM) least square method.It was compared with the traditional algorithms.The working mechanism and scanning modes for airborne lidars are similar with ground-based lidars.On the contrary,it is more difficult to retrieve the wind field because of its own flying speed,instability of flight status,the curvature of the earth,the strong ground scattering echo and ground clutter;the LM least square method is able to converge rapidly and accurately because it has the advantages of fast convergence features of the Newton-Gaussian least square method and the ensuring convergence features of the gradient descent method.The results provide references for the optimization of lidars.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Algorithm and simulation of downward velocity azimuth display of airborne wind lidars

  • 1. Management Science College, Chengdu University of Technology, Chengdu 610059, China;
  • 2. Southwest Institute of Technical Physics, Chengdu 610041, China

Abstract: In order to study how to detect three-dimension wind field under an airplane,a model for wind field was built based on ground-based wind lidars,and the radial velocity was optimized by means of Levenber-Marquardt(LM) least square method.It was compared with the traditional algorithms.The working mechanism and scanning modes for airborne lidars are similar with ground-based lidars.On the contrary,it is more difficult to retrieve the wind field because of its own flying speed,instability of flight status,the curvature of the earth,the strong ground scattering echo and ground clutter;the LM least square method is able to converge rapidly and accurately because it has the advantages of fast convergence features of the Newton-Gaussian least square method and the ensuring convergence features of the gradient descent method.The results provide references for the optimization of lidars.

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