低轨卫星缺失时序数据的模式识别方法

在航天应用中，低轨卫星经常会由于原始数据缺失而影响卫星时序数据模式识别结果，降低准确率。针对该问题提出了一种新型MR-GRU模型，可有效处理缺失时序数据，并获得较好的模式识别准确率。区别于传统模型的补全缺失数据的方法，MR-GRU模型直接在缺失时序数据上运用循环神经网络进行训练，对传统门控循环单元结构进行了改进，增加了两个新变量：掩蔽项和衰减项。掩蔽项作用于输入，衰减项作用于输入和隐层单元输出。MR-GRU模型不仅能够保持时序数据固有的时间特性，还能有效提高模式识别精度。在卫星时序数据上的模式识别试验表明，MR-GRU模型准确率优于传统模型。

A pattern recognition method for LEO satellite time series data with missing data

In aerospace applications, Low Earth Orbit (LEO) satellites often lose some parts of raw data, which will disturb pattern recognition on satellite time series data and decline accuracy. A novel MR-GRU model is proposed, which can achieve a high accuracy on satellite time series data while some data are randomly missing. The MR-GRU model directly trains a recurrent neural network on the incomplete time series data, instead of the traditional way that tries to complement the missing data. The common Gated Recurrent Unit (GRU) model is improved to MR-GRU model. Two terms are expanded, i.e. masking term and attenuation term. The masking term is applied to the input at each time, and the attenuation term is applied to the input and output of each hidden unit. Consequently, the inherent time characteristics of time series data are ensured by the MR-GRU model, while the accuracy of pattern recognition is increased. According to the experimental results on satellite time series data, it is shown that the MR-GRU model is superior to the traditional models.