基于DETR_S的卫星信号智能检测方法

李昱; 石晓然; 苗昊倩; 王晓宁; 周峰

doi:10.12263/DZXB.20240639

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基于DETR_S的卫星信号智能检测方法

电磁频谱与无人技术 | 更新时间：2025-08-18

- 基于DETR_S的卫星信号智能检测方法
- Intelligent Detection Method of Satellite Signal Based on DETR_S
- 电子学报 2025年53卷第5期页码：1365-1378
- 作者机构：
  
  西安电子科技大学电子信息对抗与仿真技术教育部重点实验室，陕西西安 710000
- 作者简介：
  
  [ "李昱男，1999年11月生，山西太原人.西安电子科技大学电子工程学院电子信息对抗与仿真技术教育部重点实验室博士研究生.主要研究方向为认知无线电、深度学习、信号检测与调制识别. E-mail: yli_1999@stu.xidian.edu.cn" ]
  [ "石晓然女，1987年7月生，河北石家庄人.西安电子科技大学电子工程学院电子信息对抗与仿真技术教育部重点实验室副教授.主要研究方向为电子侦察与智能信号处理等. E-mail: xrshi@xidian.edu.cn" ]
  [ "苗昊倩女，2000年2月生，河北邯郸人.西安电子科技大学电子工程学院电子信息对抗与仿真技术教育部重点实验室硕士研究生.主要研究方向为电子侦察以及通信信号处理. E-mail: snowwhite@uestc.edu.cn" ]
  [ "王晓宁女，1998年11月生，山东省烟台人.2024年毕业于西安电子科技大学电子工程学院.主要研究方向为电子侦察以及卫星通信信号处理. E-mail: wxn20170926@163.com" ]
  [ "周峰男，1980年1月生，河南通许人.二级教授，博导，西安电子科技大学空间科学与技术学院院长.主要研究方向为电子对抗、空间电子信息系统.入选国家级人才支持计划，在国内外相关领域主流期刊和会议上发表论文200余篇，授权发明专利50余项.获国家技术发明二等奖1项、省部级科技奖励一等奖4项.中国电子学会会员编号：E190012834F. E-mail: fzhou@mail.xidian.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61201418);博士后科学基金(2020M673346);教育部联合基金(6141A02022367);新一代电子信息热点领域的若干问题研究(YJSJ24011)
- DOI：10.12263/DZXB.20240639
  中图分类号： TN95;TP181
- 收稿：2024-07-08，
  
  修回：2025-03-08，
  
  纸质出版：2025-05-25
- 稿件说明：
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李昱, 石晓然, 苗昊倩, 等. 基于DETR_S的卫星信号智能检测方法[J]. 电子学报, 2025, 53(05): 1365-1378.

LI Yu, SHI Xiao-ran, MIAO Hao-qian, et al. Intelligent Detection Method of Satellite Signal Based on DETR_S[J]. Acta Electronica Sinica, 2025, 53(05): 1365-1378.
李昱, 石晓然, 苗昊倩, 等. 基于DETR_S的卫星信号智能检测方法[J]. 电子学报, 2025, 53(05): 1365-1378. DOI：10.12263/DZXB.20240639

LI Yu, SHI Xiao-ran, MIAO Hao-qian, et al. Intelligent Detection Method of Satellite Signal Based on DETR_S[J]. Acta Electronica Sinica, 2025, 53(05): 1365-1378. DOI：10.12263/DZXB.20240639

摘要

复杂电磁环境下卫星信号往往淹没在背景和噪声中，传统的信号检测算法在没有准确先验知识的情况下性能急剧降低，目前基于深度学习的信号检测算法往往需要依赖专家经验的数据后处理步骤，无法对信号进行端到端检测.针对上述缺陷，提出一种基于DETR_S（DEtection with TRansformer on Signal）的卫星信号智能检测方法.DETR_S以编码器-解码器架构为基础，利用Transformer网络全局建模能力捕获频谱信息，采用多头自注意力机制有效改善频谱信息长距离依赖的问题.基于匈牙利算法的预测框匹配模块摒弃了非极大值抑制的数据后处理步骤，将信号检测问题转变为集合预测问题，使模型并行输出检测结果.引入信号重构模块，将频谱重构损失函数加入损失函数中，辅助模型挖掘频谱深层表征，提升信号检测性能.实验结果表明，在仅使用信号频谱幅度信息条件下，DETR_S能够在信噪比等于0 dB及以上对卫星信号进行精确检测（>95%），优于典型的目标检测方法.

Abstract

Satellite signals in complex electromagnetic environments are often submerged in background and noise

and the performance of traditional signal detection algorithms degrades dramatically without accurate a priori knowledge. Currently

deep learning-based signal detection algorithms often require data post-processing steps that rely on expert experience and cannot achieve end-to-end detection of signals. To address the limitations of existing algorithms

an intelligent detection method of satellite signals based on DETR_S (DEtection with TRansformer on Signal) is proposed. Firstly

DETR_S is based on the coder-decoder architecture and uses the global modeling ability of the transformer network to capture spectrum information. Secondly

uses the multi-head self-attention mechanism to effectively improve the problem of long-distance dependence of spectrum information. Then

the prediction frame matching module based on the Hungarian algorithm abandons the post-processing step of data with non-maximum suppression

and transforms the signal detection problem into a set prediction problem

so that the model can output the detection results in parallel. Finally

the signal reconstruction module is introduced

and the spectrum reconstruction loss function is added to the loss function to further improve the signal detection performance by mining the deep representation of the spectrum. The experimental results show that DETR_S can accurately detect faint satellite signals (>95%) at signal-to-noise ratios of 0 dB and above using only the signal spectral amplitude information

which is a significant improvement in the detection effect compared with the typical target detection representative network.

关键词

Keywords

references

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