欧明, 甄卫民, 於晓, 徐继生, 邓忠新. 一种基于截断奇异值分解正则化的电离层层析成像算法[J]. 电波科学学报, 2014, 29(2): 345-352. doi: 10.13443/j.cjors.2013052401
      引用本文: 欧明, 甄卫民, 於晓, 徐继生, 邓忠新. 一种基于截断奇异值分解正则化的电离层层析成像算法[J]. 电波科学学报, 2014, 29(2): 345-352. doi: 10.13443/j.cjors.2013052401
      OU Ming, ZHEN Weimin, YU Xiao, XU Jisheng, DENG Zhongxin. A computerized ionospheric tomography algorithm based on TSVD regularization[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2014, 29(2): 345-352. doi: 10.13443/j.cjors.2013052401
      Citation: OU Ming, ZHEN Weimin, YU Xiao, XU Jisheng, DENG Zhongxin. A computerized ionospheric tomography algorithm based on TSVD regularization[J]. CHINESE JOURNAL OF RADIO SCIENCE, 2014, 29(2): 345-352. doi: 10.13443/j.cjors.2013052401

      一种基于截断奇异值分解正则化的电离层层析成像算法

      A computerized ionospheric tomography algorithm based on TSVD regularization

      • 摘要: 提出了一种基于截断奇异值分解正则化(Truncated Singular Value Decomposition,TSVD)的电离层层析成像算法.该算法选择球谐函数与经验正交函数作为表征电离层电子密度空间变化的基函数,以降低背景模型对层析成像的影响;利用广义交叉验证法来选择合适的截断参数,提高了算法的稳定性和反演精度.基于中国区域23个观测站的电离层层析成像仿真结果表明:与乘法代数重构算法(Multiplicative Algebraic Reconstruction Technique,MART)相比,基于TSVD正则化的电离层层析成像算法能够在不需要背景电离层电子密度作为先验条件的情况下,实现电离层电子密度的有效反演.

         

        Abstract: A computerized ionospheric tomography algorithm based on truncated singular value decomposition (TSVD) regularization is put forward for minimizing the impact of the background ionosphere model. Spherical harmonic functions and empirical orthogonal functions are utilized as the basic function to represent the spatial variation of the ionosphere. The generalized cross validation (GCV) method is used to determine the proper truncated parameter, which helps to enhance the stability and reliability of the TSVD regularization method. The comparison between multiplicative algebraic reconstruction technique (MART) and the TSVD regularization method is also made in the ionospheric tomography simulation experiment in China with 23 observations. Results verify the validity of the proposed algorithm without the background ionospheric electron density as the prior condition.

         

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