Abstract:

The swift advancement of radio technology has frequently introduced radio frequency interference(RFI) at the radio observatory site,thereby contaminating the data collected from astronomical observations.When the original IQ data and the corresponding statistical features of RFI are used as inputs to a residual neural network,the temporal discontinuity of RFI hinders the convergence of loss functions,which also diminishes the recognition accuracy.To address the challenges,this paper proposes a radio frequency interference identification method based on composite time-scale features.First,the hidden information on the data is revealed through high-dimensional mapping in the time-frequency domain,whose descriptive diversity is enhanced by the fusion of both long and short time features.Second,an RFI recognition network is constructed,which consists of three parts:a deep convolutional neural network for efficient feature extraction; a path aggregation network for combining shallow graphical features with deep semantic features; a predictive output network that integrates multi-scale features for making a decision for recognition.Experimental results show that the overall recognition accuracy of the proposed method achieves 96%,representing an improvement exceeding 30% over that obtained by using the original IQ signal as the neural network input.Therefore,the method proposed in this paper effectively addresses the issue of neural networks being difficult to train and exhibiting poor performance due to the temporal discontinuity of the signals.

Key words: radio frequency interference, neural networks, signal interference, interference recognition