Research On Work Mode Analysis And Recognition Of Airborne Phased Array Radar

As the precursor and foundation of electronic warfare,electronic reconnaissance is an important factor in determining the outcome of the war.How to use the intercepted intelligence in the complex electromagnetic environment to provide support for the electronic warfare,so as to improve the ability of electronic warfare,has become a key issue to be solved in modern electronic warfare.Taking the confrontation among space movement platforms as our research background,we mainly study the recognition of airborne phased array radar under different work mode.Based on the basic principle of radar,four typical work modes of airborne phased array radar are simulated and analyzed,and the deep neural network is used to learn and identify the characteristics of signal,hoping this work can provide support for the development of electronic countermeasure technology.The main work of this paper are as follows:1.According to the basic principle of radar,the model of airborne phased array radar is established to simulate and analysis typical work modes of radar,according to different intercept scene.The simulation contains four kinds of work modes such as track while scan,track and scan,multi-target tracking and single target tracking.Simulation results are the pulse description words intercepted by the reconnaissance aircraft,which provide basis for work mode analysis and recognition of airborne phased array radar.2.A signal amplitude fitting method based on multi-level modeling is proposed to solve the problem that the characteristics of the radar scaning are easily interrupted by noise.Multi-level modeling method is used to model the pulse description words at pulse level,pulse group level and the work mode level,effectively overcome the influence of dropped or false pulse on amplitude fitting.3.The method based on denoising autoencoder is proposed to recognize work mode of airborne phased array radar.Also we make a through analysis of the parameters belong to deep neural network,such as the number of hidden layers,the number of hidden nodes and the ratio of noise.On this basis,the recogniton rate of different deep neural networks are compared.Experimental results show that our method can not only extract the essential characteristics of different modes adaptively,but also has a good ability to adapt to the noise,which provide a new idea for recognition of radar.4.In view of the disadvantage of denoising autoencoder,costing lots of training time,a new method based on marginalized stacked denoising autoencoder is proposed.Also an information fusion method combining SVM and DS evidence theory is proposed to further improve the classification accuracy.Qualitative experiments show that the proposed method combining marginalized stacked denoising autoencoder and DS evidence theory,can not only learn more useful representations,but also achieve lower test error,which verified its the effectiveness.