Research On LPI Radar Waveform Recongnition Algorithm Based On Joint Ambiguity Function And Deep Learning

In the modern information war,it is of great significance for electronic reconnaissance to identify the intercepted radar waveform effectively and accurately.With the emergence of low probability of intercept(LPI)radar and other complex radar systems,the radar has been greatly improved in function and performance.The traditional recognition algorithm is difficult to classify it,which brings great challenges to electronic reconnaissance.In this paper,three LPI radar waveform recognition algorithms are proposed to solve the problems of the difficulty in extracting LPI radar waveform features and the low recognition rate in the complex electromagnetic environment,which include of the envelope feature of the main ridge section of the ambiguity function(AF)and the structure feature of the time-frequency image,besides that the fusion of the two kind features.The main work of the thesis are as follows:1.In the aspect of envelope feature extraction and recognition of main ridge section of ambiguity function,a radar waveform recognition algorithm based on AF main ridge section and TPOT is proposed.The algorithm first extracts the envelope and the maximum rotation angle of the AF main ridge section of the radar waveform;then extracts the envelope features of the AF main ridge section,including the dimension features and resemblance coefficient features,and combines the maximum rotation angle features to form the feature set;finally,selects and optimizes the classifier,to realize the radar waveform classification using the treebased pipeline optimization tool(TPOT).The simulation results show that the algorithm proposed in this paper has good effect on feature extraction and classification at low signal-tonoise ratio(SNR).2.In the aspect of self-learning time-frequency image structure features extraction and recognition,firstly,aiming at the problems of low SNR LPI radar waveform feature extraction and low recognition rate,an LPI radar waveform recognition algorithm based on convolution neural network(CNN)and support vector machine(SVM)is proposed.The algorithm transforms radar waveform into two-dimensional time-frequency image by time-frequency analysis,extracts the principal component information of time-frequency image by image processing algorithm,and realizes the feature extraction and recognition of radar waveform by convolutional neural network and support vector machine.Then,aiming at the problem that LPI radar waveform training samples small and deep CNN parameters training difficult,the idea of transfer learning is put forward in the radar waveform recognition algorithm.The relationship between SVM and deep network model classifier is analyzed,and two radar waveform recognition algorithms based on depth transfer learning(perception-v3-svm,resnetv2-152-svm)are proposed.The simulation results show that the proposed algorithm improves the radar waveform recognition rate under low SNR,and verifies the feasibility and effectiveness of the proposed network model in the field of LPI radar waveform recognition.3.In the aspect of joint feature recognition,aiming at the problem of low recognition rate of transfer learning model under low SNR,a network based on AF main ridge section feature and image feature depth transfer learning is designed.By fusing the envelope feature of AF main ridge section with the time-frequency image feature extracted by depth transfer learning algorithm,the LPI radar waveform classification is realized.The simulation results show that the proposed joint feature recognition algorithm improves the recognition accuracy of LPI radar waveform at low SNR.