Study Of Radar High Range Resolution Profiles Target Recognition Based On Complex Networks

The original HRRP data is complex number,which not only contains a lot of target structure information,but also has the advantages of high-speed processing and easy access.It has a high degree of attention in the RATR field and is applied in experiments in various research directions.The main research work of this paper is based on the neural network to target the recognition algorithm of radar HRRP.Most of the current radar HRRP target recognition methods use only their envelope information and to remove the phase information in order to deal with the initial phase sensitivity of the complex HRRP.How to effectively use all the information in the original complex sample is the problem we have been trying to solve.This paper focuses on the use of complex networks to investigate the complex HRRP target recognition,mainly discusses the characteristics of complex HRRP itself and the deep neural network method for HRRP target recognition.The main contents of the paper are summarized as follows: 1.We studied the basic principle of high resolution range image target recognition.The HRRP sensitivity problem and the traditional classifier design are discussed.The AGC classifier is used as an example to verify the pretreatment effect.The results also show that the pretreatment has a significant impact on the recognition results.2.We studied the statistical properties of complex high resolution range images.The characteristics of complex HRRP are introduced in detail from the mathematical point of view,the influence of initial relative target recognition is analyzed,the rationality of the independence of real and imaginary parts of complex HRRP with different range units is explained,and we designed the preprocessing steps according to the distribution characteristics of complex HRRP,which provides a theoretical basis for the follow-up study of complex network.3.We studied the radar target recognition method based on real network.Most of the traditional target recognition methods are shallow linear structures,and it is difficult to obtain deep features of the target.With the development of neural networks,many radar target recognition methods based on deep neural networks have emerged.Compared with the shallow linear method,the deep neural network can learn the effective features of the target level because of its structural characteristics,and has better recognition performance.Starting from the basic neural network,we introduce the classical convolutional neural network and residual neural network,analyze their structure and working principle in detail,and apply them to the real HRRP target recognition task,the experiment shows that the performance of deep neural network is better than the traditional RATR method.4.We studied the RATR method based on complex network.Aiming at the problem that the common target recognition method removes the phase information and causes the recognition rate to be limited,it is proposed to use the complex network and the complex residual network to identify the complex HRRP.The important components of the complex network model are introduced: complex convolution,complex excitation function,complex batch normalization,and complex residual network based on convolutional networks.On the basis of preprocessing the complex HRRP,the two-layer complex convolution network,the three-layer complex convolution network and the two-layer complex residual network are used for the target recognition experiment.Since the phase information of the complex HRRP is utilized,higher recognition rate is obtained than that of high resolution range profile target recognition based on real network.