Active Jamming Suppression for SAR Images Based on Self-Supervised Complex-Valued Deep Learning

HUA Qing-long; WEI Chen-xi; ZHANG Yun; ZHANG Qian; JI Zhen-yuan; JIANG Yi-cheng

doi:10.12263/DZXB.20220551

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Active Jamming Suppression for SAR Images Based on Self-Supervised Complex-Valued Deep Learning

PAPERS | 更新时间：2025-12-08

- Active Jamming Suppression for SAR Images Based on Self-Supervised Complex-Valued Deep Learning
- ACTA ELECTRONICA SINICA Vol. 51, Issue 4, Pages: 965-974(2023)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20220551
  CLC： TP751;
- Received：14 May 2022，
  
  Revised：2022-07-27，
  
  Published：25 April 2023
- 稿件说明：
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化青龙,魏晨曦,张云等.基于自监督复数域深度学习网络的SAR有源压制干扰抑制方法[J].电子学报,2023,51(04):965-974.

HUA Qing-long,WEI Chen-xi,ZHANG Yun,et al.Active Jamming Suppression for SAR Images Based on Self-Supervised Complex-Valued Deep Learning[J].ACTA ELECTRONICA SINICA,2023,51(04):965-974.
化青龙,魏晨曦,张云等.基于自监督复数域深度学习网络的SAR有源压制干扰抑制方法[J].电子学报,2023,51(04):965-974. DOI： 10.12263/DZXB.20220551.

HUA Qing-long,WEI Chen-xi,ZHANG Yun,et al.Active Jamming Suppression for SAR Images Based on Self-Supervised Complex-Valued Deep Learning[J].ACTA ELECTRONICA SINICA,2023,51(04):965-974. DOI： 10.12263/DZXB.20220551.

摘要

合成孔径雷达（Synthetic Aperture Radar，SAR）具有全天时、全天候、高分辨率对地观测的优势，但在成像过程中容易受到电磁干扰，进而严重影响SAR图像的判读与解译.针对复杂对抗环境下的强有源压制干扰问题，本文提出一种基于自监督复数域深度学习的SAR有源压制干扰抑制方法，以及一种新型复数域干扰抑制网络，对权值、激活函数及卷积运算等进行了复数域处理设计，挖掘SAR复数域图像中目标和干扰在幅度和相位两方面的不同信息表征，实现对干扰的抑制.同时本文提出一种自监督训练策略，解决传统网络训练过程严重依赖人工标注样本的问题，其适用于复杂干扰下样本难以标注的应用场景.开展仿真分析与实测数据验证，实验结果表明所提方法可有效地抑制复杂背景下的有源压制干扰，具有自监督智能干扰抑制能力.

Abstract

Synthetic aperture radar (SAR) has the advantages of all-day

all-weather

and high-resolution in the earth observation

but it is susceptible to electromagnetic interference during the imaging process

which seriously affects the subsequent interpretation of SAR images. To this end

this paper proposes a suppression method for SAR blanketing jamming based on self-supervised complex-valued deep learning

and proposes a novel complex-valued interference suppression network

which can make full use of the amplitude and phase information of SAR complex images. The weights

activation functions and convolution operations of the network are designed for complex domain processing

and the different information representations of target and clutter in amplitude and phase in SAR images are mined to achieve interference suppression. Meanwhile

a self-supervised training strategy is proposed to solve the problem of relying heavily on manually labeled samples in the traditional network training process

and is suitable for application scenarios where samples are difficult to be labeled under complex interference. The simulation analysis and experimental verification are carried out. The experimental results show that the proposed method can effectively suppress the active jamming of complex backgrounds

and has the ability of self-supervised intelligent interference suppression.

关键词

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references

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