Research On Radar Emitter Recognition Algorithm Based On Improved CNN

Radar emitter identification is a key link in electronic countermeasures signal processing and an important part of electronic reconnaissance and threat warning systems.However,with the rapid development of radar technology and the large number of applications of new system radars,the density and complexity of radar signals have increased significantly,which has brought great difficulties to the identification of radar emitter signals in complex systems,resulting in traditional pulse-based descriptors.The method of five-dimensional feature recognition has gradually failed.The intrapulse modulation of radar radiation source signal is an important feature of the signal,which can reduce the aliasing of the parameter space and improve the recognition rate of the radiation source.It can be used as the sixth dimension feature research.Considering the characteristics of radar signals with high density,various forms and non-stationary,this paper studies the radar source identification method based on JTFA preprocessing and convolutional neural network(CNN).Aiming at the problems existing in CNN identification of radiation sources,a radar emitter identification algorithm based on improved CNN is proposed.Through a large number of contrast experiments and simulations,the algorithm has the advantages of high recognition rate,short training time and strong generalization performance,which is more in line with the needs of modern complex electromagnetic environment.The main work of the thesis is as follows:1.Establish a radar emitter signal model,and simulate and analyze the main radar signals of the eight signals from the time and frequency domains.2.Using STFT,WVD and WT and other JTFA techniques,analyze eight kinds of radar signals such as chirp.By comparing under different SNR,The STFT can be obtained to have better performance for radar signals at low SNR.3.The radar radiation source identification method based on CNN and JTFA is studied.Based on the time-frequency diagram of the signal,the signals are identified by two CNNs,Alex and VGG16.The results show that the recognition accuracy of the Alex network is only 97.8%,but the network convergence time is short.VGG16 network recognition isaccurate up to 99%,but the network convergence time is long.Comparative analysis shows that CNN has certain defects in the identification of radiation sources and needs to improve the network structure.4.A radar emitter identification algorithm based on improved CNN is proposed.Based on the time-frequency diagram of the signal,the improved network structure is fine-tuned several times,and the improved network structure is finally designed to identify the radiation source signals.The results show that the improved network training time is shorter than VGG16,the recognition accuracy is as high as 99.4%,and even SNR below-8db,the recognition accuracy can reach more than 98.5%.The experimental results verify that the improved CNN-based radiation source identification algorithm has the advantages of high recognition rate,short training time and strong generalization performance.