基于混合型复数域卷积神经网络的三维转动舰船目标识别  

Recognition of 3D Rotating Ship Based on Mix-CV-CNN

作　　者：张云[1] 化青龙 姜义成[1] 徐丹 ZHANG Yun;HUA Qing-long;JIANG Yi-cheng;XU Dan(School of Electronics and Information Engineering,Harbin Institute of Technology,Harbin,Heilongjiang 150001,China)

机构地区：[1]哈尔滨工业大学电子与信息工程学院,黑龙江哈尔滨150001

出　　处：《电子学报》2022年第5期1042-1049,共8页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61201304,No.61201308)。

摘　　要：在较高海情下,由于舰船目标处于随机摆动的非平稳运动状态,常规合成孔径雷达(Synthetic Aperture Radar,SAR)成像处理会使得目标散焦、方位模糊,从而导致三维转动舰船目标识别准确率低.本文提出一种混合型复数域卷积神经网络(Mix-type Complex-Valued Convolutional Neural Network,Mix-CV-CNN),并推导Mix-CV-CNN前向传播与反向传播算法.三维转动舰船目标经过SAR成像处理后存在剩余相位信息,Mix-CV-CNN能充分利用SAR复数域图像的幅度和相位信息,在不进行目标重聚焦的情况下,较好完成SAR复杂运动舰船目标的识别.实验表明,Mix-CV-CNN相较于具有相同自由度的实数域卷积神经网络(Real-Valued Convolutional Neural Network,RV-CNN)识别性能有所提高,实测数据识别平均准确率提高3.85%.Because the ship targets are in a non-stationary motion state of random swing,conventional synthetic aperture radar(SAR)imaging processing will make the targets defocused and azimuth blurred,resulting in the recognition accuracy of three-dimensional rotating ship.This paper proposes a mixed-type complex-valued convolutional neural network(Mix-CV-CNN)and derives the Mix-CV-CNN forward propagation and backpropagation algorithms.The three-dimensional rotating target has residual phase information after SAR imaging processing.The Mix-CV-CNN could make full use of the amplitude and phase information of the complex SAR image and could better complete the recognition of SAR three-dimensional rotating targets without target refocusing.The experimental results show that Mix-CV-CNN has improved recognition performance compared with the real-valued convolutional neural network(RV-CNN)with the same degree of freedom.The average accuracy is increased by 3.85%.

关 键 词：合成孔径雷达 复数域卷积神经网络 三维转动 目标散焦 舰船目标识别 混合型复数域卷积神经网络 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]