不同波长近红外激光大气消光特性的泛化回归神经网络反演算法

由于大气的不均匀性和不稳定性,采用MODTRAN等大气传输软件计算,很难保证实时精确地获取某波段的近红外激光大气消光系数、大气透过率等消光特性,且传统的手段存在很多不确定性因素.利用近红外波段激光不同波长大气消光特性之间的相关性和泛化回归神经网络,可以实现输入到输出之间非线性的相关性函数关系,通过已知波长的激光雷达实测数据来实时反演其他波长的大气消光特性.实践表明,该方法为实时获取实战条件下某一波段近红外大气消光特性提供一种新方法.

Real time measurement of atmospheric optical properties

Because of the nonuniformity and instability of atmosphere, it is very difficult to obtain real timely and precisely a band of infrared atmospheric extinction coefficient with MODTRAN atmospheric transmission software and any other traditional methods. The nonlinear function relationship between the input and output of neural network can be obtained via the relation among the atmospheric optical properties at different wavelengths in the near infrared band by generalized regression neural network. Then the atmospheric optical properties at other wavelengths can be further derived through real time inversion of the data at certain wavelength measured by laser radar. The method was proved to be effective in obtainning real timely the near infrared atmospheric optical properties of a band in actual combat conditions.