Near-real-time Ionospheric TEC Derivation from Single Station with Neural Network

In derivation of ionospheric total electron content (TEC) by global navigation satellite system (GNSS), compared with a multi-station observation network, single-station derivation is a flexible and convenient method. Based on the artificial neural network (ANN), we propose a near-real-time method to derive ionospheric TEC with a single station. In this method, the instrumental biases of previous period are taken as initial values and are adjusted with the observation data. Meanwhile, the ionospheric TEC is derived in near-real-time. In order to have a detailed assessment on this method, through a single station in China, the TEC during four magnetically quiet days is derived by the proposed method and classic least square method (LSM) with spherical harmonics, respectively. The references of instrumental biases and TEC are obtained by the nearby multi-station network. In another test, through a single station in Europe, the TEC during an ionospheric storm and the quiet days before and after the event are also derived by the above methods. At the magnetically quiet days, the estimated instrumental biases are closer to the references than LSM with spherical harmonics on the whole, and the derived TEC is also closer to the references. During the ionospheric storm, the TEC derived by the two methods also have good consistency, and the estimated instrumental biases during the ionospheric storm are closer to those at magnetically quiet days. The results show that the proposed neural network method has higher accuracy than LSM with spherical harmonics.