基于残差网络的微波关联成像目标重构方法

A Microwave Correlation Imaging Reconstruction Method Based on Residual Block

微波关联成像将量子强度关联成像的思想扩展到微波领域,不仅很好地解决了传统雷达无法进行高分辨凝视成像以及复杂的运动补偿等问题,还具有分辨率高、抗干扰能力强等特点,受到广泛关注。针对微波关联成像传统重构算法在低采样数条件下重构质量差问题,将残差网络和卷积神经网络应用于微波关联成像重构中,提出一种基于残差网络的微波关联成像方法,以雷达接收机回波数据作为网络的输入,依次通过训练好的特征提取网络和图像增强网络,进行高质量图像反演,并将文中算法与伪逆算法和压缩感知算法进行仿真对比分析。仿真结果表明:在相同采样率下,文中算法成像质量均高于其他算法。同时,在不牺牲图像质量的条件下,单张图像重构执行程序所耗时间约为0.06 s,提高了图像重建的速度,对工程应用有重要意义。

Microwave correlation imaging extends from the idea of quantum intensity correlation imaging to the microwave field,and this not only solves the problems of high-resolution staring imaging and complex motion compensation in traditional radars,but also has the characteristics of high resolution and strong anti-interference ability,and extensive attention.In view of the high sampling number and poor reconstruction quality of traditional microwave correlation imaging reconstruction algorithms,this paper proposes a reconstruction method of microwave correlation imaging based on convolutional neural network and residual network.The radar receiver echo data are used as the input of the network,after the initial reconstruction,the trained feature extraction network and the image enhancement network are used for feature extraction and feature enhancement in order to perform image reconstruction.The algorithm in this paper is compared with pseudo-inverse algorithm and compressed sensing algorithm.The simulation results show that compared with the existing optimal microwave correlation reconstruction compressed sensing algorithm,the image reconstructed by this algorithm has more advantages.At the same time and without sacrificing image quality,the expending time to execute a single image reconstruction program is about 0.06 s,improving the speed of image reconstruction and having great significance to engineering applications.