Research and Application of Recurrent Neural Network in Solar Radio Interference Filtering

The various interfering signals present in the space result in the inability to obtain a clean and clear solar radio dynamic spectrum, which affects the effective observation of solar radio burst events. At the data processing level, we propose a method for predicting and processing radio interference signals in solar radio burst events using recurrent neural network in deep learning. Firstly, the radio signal that satisfies the condition is selected by the amount of solar radio flux of all frequency channels at some moment, and then the position of the initial time of the burst event is located by using the variation curve of the solar radio flux with time under the single frequency channel. After that, the constructed recurrent neural network is used to predict the signal value of the radio in the burst area. Finally, according to the linear additivity of the signal, the value of the clean pure burst event is obtained by subtracting the predicted radio value from the original value of the burst area. The experimental results show that the proposed method can effectively remove the interference in the solar radio dynamic spectrum and preserve the effective information of the burst event to the greatest extent. This provides a new idea and research direction for deep learning in the anti-interference processing of astronomical big data.