Radar Interference Recognition Based On Deep Learning

With the rapid development of modern electronic warfare technology,the new type of active interference that radar faces is characterized by low power,high coherence and strong deception,which poses a huge threat to the survival and operation of radar.In order to suppress interference,the radar first needs to identify the interference so that can take targeted anti-interference measures.However,traditional interference recognition methods require manual analysis and extraction of various features,which are poor in generality and weak in generalization,and it is difficult to adapt to rapid changes of complicated adversarial environment,so it is urgent to propose a more robust and intelligent interference identification method.For the recognition of radar active interference,this paper respectively uses convolutional neural network algorithm and generative adversarial network algorithm to realize supervised and semi-supervised learning of interference,its results are better than traditional methods.The main work of this paper focuses on the modeling and analysis of interference,dataset design,interference feature extraction and recognition algorithm.The specific research content is as follows:1.Model and analyze the 9 types of typical radar active interference,analyze their characteristics of time domain,frequency domain and time-frequency domain,and lay a theoretical foundation for their identification.2.Interference signals deep learning requires a unified format dataset.The paper simulates various types of interference signals,and performs them through time-frequency conversion,normalization,smooth filtering,adaptive cropping and other processing to get the unified format feature,and obtain training and test datasets of different JNR and different parameters.3.The paper presents the interference recognition process based on signal feature extraction,analyzes and selects the multi-domain features of the interference signal and applies them to the support vector machine algorithm to achieve the classification of interference.The simulation results prove that the selected features and classification methods have a good classification effect on the 9 types of interference,which can be used as a control for deep learning methods.4.Traditional methods require artificial feature extraction and sensitivity to changes in interference parameters.To solve this problem,this paper uses convolutional neural network(CNN)deep learning methods to achieve supervisedlearning of interference based on a unified format dataset.Simulation shows that its overall recognition rate is significantly improved and model has better robustness compared with traditional methods.Secondly,the improved model GAP-CNN keeps a high recognition rate under the condition of greatly reducing network parameters and save much training time,and improves application potential of the radar interference deep learning.5.Interference samples are difficult to be widely labeled in actual application scene,So it is necessary to learn from a small amount of labeled interference and a large number of unlabeled interference.In this paper,semi-supervised generative adversarial nets(SSGAN)and GC-SSGAN is used to implement semi-supervised learning of interference samples,and a suitable discriminator and generator is designed.The model learns effectively under a lower labeling rate(less than 2%)samples,and achieves a high recognition rate(greater than 0.8).Compared with CNN model under the same conditions,the recognition rate is significantly improved.