Radar Signal Modulation Recognition Technology Based On Time-Frequency Image

Radar emitter signal recognition is an important link between the electronic countermeasure and the electronic reconnaissance,which plays a significant role and has a high status in the electronic warfare.Only fully grasping the information of the enemy's radar emitter can we win the initiative of the modern war,and meanwhile occupy the commanding heights of the electronic warfare,which makes the radar emitter recognition become an important research direction in the modern electronic reconnaissance system.This paper makes a big deeper study of key technologies including the radar emitter modeling,the time-frequency analysis,the image preprocessing,the feature extraction,the support vector machine and the deep convolutional neural network.The main research contents are summarized as follows:Firstly,the radar emitter signal model and the time-frequency analysis technology are studied.Not only should the common radar emitter signal model be constructed,but also the time-frequency analysis method of non-stationary signals is discussed.Furthermore,the nine modulated signals' time-frequency graphes,based on Choi-Williams Distribution,are given under low noise.Secondly,a kind of support vector machine radar signal modulation recognition algorithm,based on the time-frequency image,is studied.Moreover,the time-frequency image preprocessing and feature extraction technology are systematically expounded.Based on the analysis of the related principles of support vector machines,this part presents the existing problem of the decrease of classification results when the parameters are unsuitable for the classification of support vector machines.Though the improved particle swarm optimization algorithm,both the local optimal solution and the global optimal solution are searched.This algorithm also overcomes the defect that the traditional optimization method is easy to fall into the local optimal solution,and combines the four types of image features as recognition features.In addition,a new improved PSO radar signal modulation recognition algorithm is proposed and further improved the accuracy rate of the modulation recognition.Next,the convolution neural network modulation recognition algorithm,based on time-frequency images,is studied.The principle and basic structure of convolutional neural network are briefly introduced.The realization method of convolution neural network is analyzed.The pre-training convolution neural network model is discussed.Moreover,combined with the migration learning theory,two novel FT-GoogLeNet-icp4-SVM and FT-VGGNet-fc6-SVM modulation recognition algorithms are proposed respectively.The advantages of the pre-training convolution neural network model in multi-classification model to extract the paremeter are fully explored,which solves the problem of difficult training of small-scale deep network samples and reduces the training time and the training complexity to a certain extent.The simulation results show that the proposed algorithm significantly improves the recognition performance of the system.Finally,the multi-signal modulation recognition technology,based on time-frequency images,is studied.The principle of fractional Fourier transform is summarized.The realization of fast DFRFT method is researched.A joint classifier multi-signal modulation recognition is proposed.The algorithm realizes the efficient separation of multiple signals and overcomes the problem of low recognition rate due to the incomplete information of multiple signals under low signal-to-noise ratio,which provides a new modulation recognition for multi-component signals ideas.