Radar Image Target Detection Based On Electromagnetic Scattering Characteristics

With the development of radar high-resolution imaging methods,high-resolution imaging radar can obtain images with resolution better than 1m?1m.The development of imaging methods provides strong support for the improvement of ground reconnaissance capability.On the premise of high resolution of radar image,the convolutional neural network can be used to extract features efficiently and accurately detect maneuvering targets in clutter environment.The traditional methods of radar image target detection have some limitations,such as position sensitivity,speckle noise sensitivity,resolution sensitivity and attitude sensitivity.In this paper,the convolution neural network is used to extract the features,and a radar target detection method based on electromagnetic scattering information is proposed.This paper not only extracts the amplitude information of the radar echo signal,but also makes full use of the phase information in the echo signal,that is,the unique characteristics of the radar signal.The electromagnetic scattering information is integrated into the convolution neural network,and the deep learning method is used to extract the semantic information of radar signal from the shallow to the deep layer,which solves the problem that only the pixel information of image is extracted but not the radar echo data phase information in the previous radar image target detection,so as to improve the detection accuracy and robustness of the system.The main contents of this paper are as follows:1.Pixels of the same component are discrete in radar image target detection,which makes it difficult to extract target features and there is noise sensitivity problem also,the scattering center model based on electromagnetic scattering mechanism is analyzed then.The scattering center of target attribute is extracted by image domain method,and the target in radar image is segmented at component level,then the segmented target at component level is fused with the original target image.The fusion is at the feature level in convolutional neural network.On the premise of ensuring the integrity of mobile target components,the features of the original radar image are extracted by convolutional neural network,and the target is detected on the basis of fusion network.2.In order to solve the problems of traditional radar target detection methods,such like that the traditional radar target detection needs to extract features by artificial design,which needs strong professionalism.The other problem of traditional radar target detection methods is that it has poor robustness in complex environment.So in this paper,the method of radar image target recognition based on deep convolution neural network is studied.On the basis of manual annotation,the convolution neural network is used to automatically extract the unique features of the target,so as to achieve the accurate classification and location of different types of maneuvering targets.3.In the radar image target detection,only the amplitude information of radar echo data is used instead of the abundant phase information in the echo data,which is the electromagnetic scattering information in the scattering center model,so the unique electromagnetic scattering information of radar data is lost.To solve this problem,in this paper,the amplitude information and phase information of radar echo data are extracted simultaneously,and they are connected as different depth layers of input tensor.At the same time,they are input into deep convolution neural network to improve the performance of radar image target recognition.