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

张云; 化青龙; 姜义成; 徐丹

doi:10.12263/DZXB.20211255

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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

学术论文 | 更新时间：2025-12-08

- 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
- 电子学报 2022年50卷第5期页码：1042-1049
- 作者机构：
  
  哈尔滨工业大学电子与信息工程学院，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "张云女，1975年11月出生于黑龙江省虎林市.现为哈尔滨工业大学教授、博士生导师.主要研究方向为雷达信号处理、SAR成像、机器学习和遥感模式分析.E-mail: zhangyunhit@hit.edu.cn" ]
  [ "化青龙男，1995年2月出生于安徽省阜阳市.现为哈尔滨工业大学博士.主要研究方向为雷达图像处理和深度学习网络框架.E-mail: huaqinglong_hit@163.com" ]
  [ "姜义成男，1964年11月出生于黑龙江省哈尔滨市.现为哈尔滨工业大学教授、博士生导师.主要研究方向为雷达信号处理.E-mail:jiangyc@hit. edu. cn" ]
  [ "徐丹女，1996年4月出生于黑龙江省七台河市.主要研究方向为数据分析与智能图像处理.E-mail: xudanhit@hit.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61201304;61201308)
- DOI：10.12263/DZXB.20211255
  中图分类号： TP751
- 收稿：2021-09-13，
  
  修回：2022-02-20，
  
  纸质出版：2022-05-25
- 稿件说明：
移动端阅览
张云,化青龙,姜义成等.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[J].电子学报,2022,50(05):1042-1049.

ZHANG Yun,HUA Qing-long,JIANG Yi-cheng,et al.Recognition of 3D Rotating Ship Based on Mix-CV-CNN[J].ACTA ELECTRONICA SINICA,2022,50(05):1042-1049.
张云,化青龙,姜义成等.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[J].电子学报,2022,50(05):1042-1049. DOI： 10.12263/DZXB.20211255.

ZHANG Yun,HUA Qing-long,JIANG Yi-cheng,et al.Recognition of 3D Rotating Ship Based on Mix-CV-CNN[J].ACTA ELECTRONICA SINICA,2022,50(05):1042-1049. DOI： 10.12263/DZXB.20211255.

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%.

Abstract

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%.

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references

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