An outlier detection method with CNN for BeiDou MEO moderate-energy electron data

Abstract BeiDou Medium Earth Orbit moderate-energy electron detection data play an important role in space environment effect analysis including satellite anomaly diagnosis, satellite risk estimation, etc. However, the data contain outliers which cause obstacle for the subsequent usage significantly. To solve this problem, we propose an outlier detection method based on convolutional neural networks (CNNs) which can learn a rule from labeled historical data and detect outliers from the detection data. With this method, we can identify outliers and do some follow-up operations to improve the data quality. In comparison with general methods, this CNN method provides a more reliable and rapid way to build dataset for the follow-up work.