Application of Deep Clustering Algorithm in Target Echo Classification of SuperDARN Radar

SuperDARN radar target echoes usually contain echoes of various types of scattering, such as ionospheric irregularities, ground/sea scatter echoes, polar mesosphere summer echoes and meteor trail echoes. The ionospheric echoes collected by SuperDARN radar are used to map the ionospheric convection to study the large-scale dynamics of the magnetosphere-ionospheric system, which is of great significance for space weather observation and exploration. In general, the scattered ionospheric echoes received by SuperDARN are often mixed with the scattered echoes from the ground or the sea, resulting in inaccurate ionospheric convection maps. Therefore, cluster analysis of SuperDARN target echoes is of great significance. Traditionally, ground/sea scattering is determined by the lower threshold of the combination of velocity and spectral width, but the scope of use of this method is limited, and there is ambiguity for the echoes in the mid-latitude region. In order to avoid the influence of latitude conditions and reduce the omission of useful information in the data, multiple data features of the radar target echo are collected as much as possible, such as line-of-sight Doppler velocity, the spectral width, backscatter power and the elevation angle of arrival. In this paper, the graph embedding deep clustering algorithm based on autoencoder network is applied to SuperDARN target echo data for the first time, and SuperDARN echo data is effectively classified. In addition, two different types of machine learning clustering algorithms are introduced to compare with this model. The deep clustering model, traditional classification algorithm and machine learning clustering algorithm are applied to the same echo data set, and the clustering results of different clustering algorithms are compared. The application of different clustering models on sample data sets and the evaluation of clustering indexes show that the deep clustering algorithm can capture the deep structural features of the echo data, effectively compress and reduce the dimensionality of the high-dimensional data set, make full use of the useful information in the data set, and improve the precision of the target echo data clustering of SuperDARN radar.