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Iteratively regularized Gauss-Newton method for atmospheric remote sensing

机译:迭代正则化高斯-牛顿法用于大气遥感

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摘要

In this paper we present an inversion algorithm for nonlinear ill-posed problems arising in atmospheric remote sensing. The proposed method is the iteratively regularized Gauss-Newton method. The dependence of the performance and behaviour of the algorithm on the choice of the regularization matrices and sequences of regularization parameters is studied by means of simulations. A method for improving the accuracy of the solution when the identity matrix is used as regularization matrix is also discussed. Results are presented for atmospheric temperature retrievals from a far infrared spectrum observed by an airborne uplooking heterodyne instrument.
机译:在本文中,我们提出了一种针对大气遥感中出现的非线性不适定问题的反演算法。所提出的方法是迭代正则化的高斯-牛顿法。通过仿真研究了算法性能和行为对正则化矩阵选择和正则化参数序列的依赖性。还讨论了一种在将单位矩阵用作正则化矩阵时提高解的精度的方法。给出了从空中俯视外差仪观测到的远红外光谱中大气温度恢复结果。

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