一种基于联合变分自编码器的卫星重力数据粗差探测方法研究

A Gross Error Detection Method of Satellite Gravity Data Based on Joint Variational Autoencoder

在传统卫星观测数据处理运行模式下,卫星重力梯度数据粗差探测常伴随着准确率不足、海量数据计算效率低等问题。以变分自编码器在多模态数据整合分析的显著特性,构建了一种结合变分自编码器与门控循环单元网络的智能化粗差探测方法。首先,基于EGM96模型生成模拟数据集,依据变分自编码器捕获训练集数据序列的有效特征,结合门控循环单元结构对数据集进行预测,然后,采用自适应距估计优化算法作为网络模型优化器,自动寻找损失函数的最佳收敛,最后,将通过测试的训练模型应用于中国民用重力卫星的实测重力梯度数据处理。结果表明,经训练后的网络快速精确构建了训练集样本特征,实现了快速高效的粗差探测能力,各分量探测准确率达到98%以上,所提模型可有效应用于中国自主卫星重力任务的数据预处理工作。

Objectives Under the traditional satellite observation data processing operation mode,outlier detection methods in gravity gradiometry have the problems of insufficient accuracy,low efficiency,and so on.Methods This paper constructs an intelligent outlier detection method,which combines variational autoencoder and gated recurrent unit by the significant characteristics of variational autoencoders in multi-modal data integration analysis.First,on the basis of the origin and propertier of satellite gravity gradionmetry outliers,the gravity gradiometry dataset with outlier are simulated.Second,the network model captures the effective features of dataset by variational autoencoder,makes predictions on dataset by combining with gated recurrent unit,and automatically finds optimal convergence of the loss function by designing adaptive moment estimation as optimizer.Finally,the tested training model is applied to actual gravity observation data of civilian gravity satellites.Results The accuracy of the proposed model in outlier detection reaches more than 98%,with a good detection effect on both discrete and regional gross errors.Conclusions The trained network can quickly and accurately construct the sample features of the simulated gravity dataset,achieve fast and efficient gross error detection capabilities,and be effectively applied in data preprocessing of autonomous satellite gravity missions.