Feature Extracted-Based Method Against Radar Interference via Deep Learning

The traditional nonparametric / parametric interference suppression methods have some problems, such as weak adaptive ability and the need for interference detection. how to apply the data-driven advantages of deep learning technology to radar interference suppression and realize intelligent interference suppression is a very meaningful and challenging research topic. Considering the anomaly of interference at the feature level of neural networks, a lightweight interference joint detection and suppression model based on feature space distance measurement is proposed in this paper. It can realize interference detection, time frequency positioning and suppression by limiting the normal radar echo feature vector to shrink within the preset hypersphere and minimizing the hypersphere, the results of simulation data and measured data show that the proposed method has low computational complexity and can achieve synchronous interference detection and accurate suppression.