With the widespread application of global navigation satellite system, increasing amounts of gridded tropospheric data have been generated, increasing the difficulty of real-time transmission. We present that the global tropospheric grid data (GTGD) provided by Vienna mapping functions open access data have approximate sparse characteristics, and the compressed sensing (CS) method is used for sparse reconstruction for the first time. To reduce the memory and number of calculations required for the K-SVD (K-means and SVD) algorithm, the mini-batch K-SVD algorithm is proposed to speed up the calculation process. This article discusses several key problems of CS processing and application in GTGD, such as signal sparsity, reconstruction accuracy, iteration times, etc. We use mini-batch K-SVD algorithm train 1436 GTGD historical files from 2018 to establish a sparse representation model. To evaluate the accuracy of the new model, sparse reconstruction is performed on 1460 GTGD files from 2019. The experimental results show that the average root-mean-square (RMS) error, the BIAS, the maximum absolute error (MAAE), and the mean absolute error (MAE) of the compressed sensing are 2.16, 2.00E-04, 17.99, and 1.44 mm, respectively. The average RMS, BIAS, MAAE, and MAE of the spherical harmonic expansion (72-degree) are 27.39, − 9.71E-14, 273.93, and 16.67 mm. The results show that the CS approach yields a more accurate solution than spherical harmonic expansion. In summary, the established sparse representation model saves real-time transmission cost and enables high-precision sparse reconstruction and can achieve data encryption and compression simultaneously.

随着全球导航卫星系统的广泛应用，已经产生了越来越多的网格化对流层数据，从而增加了实时传输的难度。我们提出，由维也纳映射函数开放访问数据提供的全球对流层网格数据（GTGD）具有近似的稀疏特征，而压缩感知（CS）方法首次用于稀疏重构。为了减少K-SVD（K均值和SVD）算法所需的内存和计算数量，提出了小批量K-SVD算法以加快计算过程。本文讨论了GTGD中CS处理和应用中的几个关键问题，例如信号稀疏性，重构精度，迭代时间等。我们使用2018年的小批量K-SVD算法训练1436 GTGD历史文件来建立稀疏表示模型。为了评估新模型的准确性，从2019年开始对1460个GTGD文件进行稀疏重建。实验结果表明，平均均方根（RMS）误差，BIAS，最大绝对误差（MAAE）和压缩传感的平均绝对误差（MAE）分别为2.16、2.00E-04、17.99和1.44 mm。球形谐波膨胀（72度）的平均RMS，BIAS，MAAE和MAE为27.39，-9.71E-14、273.93和16.67毫米。结果表明，与球形谐波扩展相比，CS方法可提供更准确的解决方案。总之，