基于分段互相关谱图和SD-YOLOv9的Starlink下行信号检测算法

刘永军; 沈雷; 韩煜; 沈霖

doi:10.11959/j.issn.1000-0801.DXKX260050

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基于分段互相关谱图和SD-YOLOv9的Starlink下行信号检测算法

更新时间：2026-07-23

- 基于分段互相关谱图和SD-YOLOv9的Starlink下行信号检测算法
- A Starlink Downlink Signal Detection Algorithm Based on Segmented Cross-Correlation Spectrograms and SD-YOLOv9
- 电信科学 2026年
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.中国电子科技集团有限公司第三十六研究所，浙江嘉兴 314000
  3.杭州第一技师学院，浙江杭州 310023
- 作者简介：
  
  刘永军（2000- ），男，杭州电子科技大学通信工程学院硕士生，主要研究方向为数字信号处理,卫星通信。
  沈雷（1979- ），男，博士，杭州电子科技大学通信工程学院教授，主要研究方向为图像处理和信号处理。
  韩煜（1975- ），男，博士，正高级工程师，硕士生导师，主要研究方向为通信信号处理、卫星通信。
  沈霖(1985-)，女，硕士，杭州第一技师学院高级讲师，主要研究方向为信息通信。
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260050
  中图分类号： TP393
- 收稿：2026-01-16，
  
  修回：2026-04-01，
  
  录用：2026-07-14，
- 稿件说明：
移动端阅览
刘永军, 沈雷, 韩煜, 等. 基于分段互相关谱图和SD-YOLOv9的Starlink下行信号检测算法[J/OL]. 电信科学, 2026.

Liu Yongjun, ShenLei, HanYu, et al. A Starlink Downlink Signal Detection Algorithm Based on Segmented Cross-Correlation Spectrograms and SD-YOLOv9[J/OL]. Telecommunications Science, 2026.
刘永军, 沈雷, 韩煜, 等. 基于分段互相关谱图和SD-YOLOv9的Starlink下行信号检测算法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260050.

Liu Yongjun, ShenLei, HanYu, et al. A Starlink Downlink Signal Detection Algorithm Based on Segmented Cross-Correlation Spectrograms and SD-YOLOv9[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260050.

摘要

针对Starlink低轨卫星下行通信中因卫星高速运动引起的显著多普勒频偏以及低信噪比环境，导致传统正交频分复用（orthogonal frequency division multiplexing，OFDM）信号检测方法可靠性显著下降的问题，本文提出一种基于分段互相关谱图和Starlink-Detect YOLOv9（SD-YOLOv9）网络相结合的Starlink下行信号检测新算法。所提算法首先利用Starlink信号主同步序列（Primary Synchronization Signal

PSS）的重复结构，通过分段互相关与非相干累加生成具有显著谱线结构的二维特征图，将传统一维信号峰值检测问题转化为二维图像特征识别任务；此外，设计了一种名为SD-YOLOv9的增强型目标检测网络，在Backbone末尾引入STCSPA (Swin Transformer-Enhanced CSP with Attention)模块，将Swin Transformer的局部窗口注意力机制嵌入Cross Stage Partial（CSP）框架，以增强特征建模与上下文表达能力；同时将Backbone和Neck结构中传统的下采样卷积模块替换为SD-ADown (Adaptive Downsampling) 模块，实现自适应多分支特征融合，减少特征损失并提升弱信号检测性能。实验结果表明，在AWGN信道的-13 dB极低信噪比条件下，本文方法的检测准确率高达100%，相较于基准YOLOv9模型、基于互相关和CapNN算法分别提升了7%和48%，且相较于传统单一的差分互相关、直接互相关及分段互相关算法分别提升了99%、63%和41%。同时，消融实验进一步验证了STCSPA与SD-ADown模块的有效性。本研究在解决高动态无线通信检测难题方面的有效性与优越性，为下一代卫星通信系统提供了可靠的技术思路。

Abstract

To address the problem that significant Doppler frequency offsets caused by high-speed satellite movement and low signal-to-noise ratio (SNR) environments severely degrade the reliability of traditional orthogonal frequency division multiplexing (OFDM) signal detection methods in Starlink low Earth orbit (LEO) satellite downlink communications

this paper proposes a novel Starlink downlink signal detection algorithm integrating segmented cross-correlation spectrograms with the Starlink-Detect YOLOv9 (SD-YOLOv9) network. The proposed algorithm first utilizes the repetitive structure of the Starlink Primary Synchronization Signal (PSS) to generate two-dimensional (2D) feature maps with distinct spectral line structures via segmented cross-correlation and non-coherent accumulation

effectively converting the traditional one-dimensional (1D) signal peak detection problem into a 2D image feature recognition task. Furthermore

an enhanced object detection network named SD-YOLOv9 is designed. Specifically

an STCSPA (Swin Transformer-Enhanced CSP with Attention) module is introduced at the end of the Backbone to embed the local window attention mechanism of the Swin Transformer into the Cross Stage Partial（CSP）framework

thereby enhancing feature modeling and context representation capabilities. Simultaneously

the conventional downsampling convolution modules in the Backbone and Neck structures are replaced with SD-ADown (Adaptive Downsampling) modules to achieve adaptive multi-branch feature fusion

minimizing feature loss and improving detection performance for weak signals. Experimental results demonstrate that in the AWGN channel at an extremely low SNR of -13 dB

the detection accuracy of the proposed method reaches 100%. This represents an improvement of 7% and 48% compared to the baseline YOLOv9 model and the cross-correlation and CapNN-based algorithm

respectively. Additionally

it provides performance gains of 99%

63%

and 41% compared to traditional singular differential cross-correlation

direct cross-correlation

and segmented cross-correlation algorithms

respectively. Ablation studies further validate the effectiveness of the STCSPA and SD-ADown modules. The effectiveness and superiority of this study in resolving detection challenges in high-dynamic wireless communications provide reliable technical insights for next-generation satellite communication systems.

关键词

Keywords

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