基于相关-聚类的星链信号解调方法

吴超; 李力; 李亚峰; 江城; 曹延; 胡淼; 赵宜楠

doi:10.12263/DZXB.20250796

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基于相关-聚类的星链信号解调方法

学术论文 | 更新时间：2026-07-30

- 基于相关-聚类的星链信号解调方法
- Demodulation Method for Starlink Signals Based on Correlation and Clustering
- 电子学报 2026年54卷第4期页码：1857-1871
- 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.北京信息科技大学自动化学院，北京 100192
  3.北京邮电大学信息与通信工程学院，北京 100876
  4.北京中天飞创电子技术有限公司，北京 100080
- 作者简介：
  
  吴超男，1988年7月出生于河北省邢台市。现为杭州电子科技大学通信工程学院副教授、硕士生导师。主要研究方向为导航信号处理及欺骗干扰检测。中国电子学会会员编号：E190183970M。E-mail: wuchaoid@126.con
  李力男，2002年2月出生于广西壮族自治区岑溪市。现为杭州电子科技大学硕士研究生。主要研究方向为导航信号处理。E-mail: 242080141@hdu.edu.cn
  李亚峰男，1985年8月出生于山西省忻州市。现为北京信息科技大学自动化学院副教授、硕士生导师。主要研究方向为无线电/GNSS/惯性导航、多源组合导航及人工智能技术在导航领域的应用基础研究。E-mail: lyf8118@126.com
  江城男，1982年4月出生于江西省赣州市。现为北京邮电大学研究员，杭州电子科技大学兼职教授。主要研究方向为通信导航一体化。E-mail: jiangcheng@bupt.edu.cn
  曹延男，1982年10月出生于河北省石家庄市。现为北京中天飞创电子技术有限公司总工程师。主要研究方向为信号侦查、电子对抗产品研发。E-mail: 18603118997@163.com
  胡淼男，1982年出生于浙江省温州市。现为杭州电子科技大学通信工程学院副院长。主要研究方向为卫星通信、遥感、光子信息等。中国电子学会会员编号：E190159037M。E-mail: miao_hu@hdu.edu.cn
  赵宜楠男，1977年出生于黑龙江省哈尔滨市。现为杭州电子科技大学教授、博士生导师。主要研究方向为无人系统的定位与感知技术、新体制雷达、智能信号处理等。E-mail: zhaoyinan@hdu.edu.cn
- 基金信息：
  
  国家自然科学基金(61901154);浙江省自然科学基金(LQ19F010006);惯性测量全国重点实验室对外开放基金(KFJI2024005)
- DOI：10.12263/DZXB.20250796
  中图分类号： TN911.72;TN967.1
- 收稿：2025-09-12，
  
  录用：2026-04-01，
  
  网络首发：2026-05-22，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
吴超, 李力, 李亚峰, 等. 基于相关-聚类的星链信号解调方法[J]. 电子学报, 2026, 54(04): 1857-1871.

WU Chao, LI Li, LI Yafeng, et al. Demodulation Method for Starlink Signals Based on Correlation and Clustering[J]. Acta Electronica Sinica, 2026, 54(04): 1857-1871.
吴超, 李力, 李亚峰, 等. 基于相关-聚类的星链信号解调方法[J]. 电子学报, 2026, 54(04): 1857-1871. DOI：10.12263/DZXB.20250796

WU Chao, LI Li, LI Yafeng, et al. Demodulation Method for Starlink Signals Based on Correlation and Clustering[J]. Acta Electronica Sinica, 2026, 54(04): 1857-1871. DOI：10.12263/DZXB.20250796

摘要

随着巨型低轨卫星（Low Earth Orbit，LEO）星座的迅速发展，大量星基机会信号为全球导航卫星系统（Global Navigation Satellite System，GNSS）拒止条件下的辅助与替代性导航需求提供了潜在解决方案。以星链信号为例，对其进行时频估计并完成有效解调，是实现辅助导航的必要前提。然而，星链卫星的高速运动引起显著的多普勒效应，为信号时频估计精度与可靠解调带来技术挑战。针对现有的时频估计算法在低信噪比（Signal-to-Noise Ratio，SNR）条件下鲁棒性不足，传统算法在高动态环境下难以实现稳定解调的问题，本文提出了一种基于相关-聚类的星链信号解调方法，并在仿真与实测数据上进行实验验证。利用星链下行信号特殊的同步序列结构，设计相关性增强的延迟自相关定时算法与改进的符号内相位差频偏估计算法，实现同步序列定时与多普勒系数的粗估计。提出基于K-means聚类的逐符号时频精估计方法，在无数据符号先验的条件下提取星座聚类统计量，并以经验信噪比（Empirical Signal-to-Noise Ratio，ESNR）最大化为准则完成残余时频误差精估计。构建仿真场景，分析所提方法的收敛性与估计精度，并基于实测星链下行信号，以解调误比特率（Bit Error Rate，BER）为指标评估所提算法的性能。实验结果表明，所提方法在高动态条件下具有良好的收敛性与估计精度，且在7.3 dB SNR实测数据下，解调BER为0.88%，相较对比算法降低55.10%，在高动态环境下显著提升了时频估计与解调的精度，为星链机会信号导航提供了技术支撑。

Abstract

With the rapid development of mega low earth orbit (LEO) satellite constellations

an abundance of signals of opportunity have emerged as potential solutions for assisting global navigation satellite system (GNSS) navigation in GNSS-denied environments. Taking Starlink signals as a representative example

accurate time-frequency estimation and reliable demodulation are essential prerequisites for navigation applications. However

the high dynamics of LEO satellites introduce severe Doppler effects

which challenge the accuracy and robustness of time-frequency estimation and the reliability of demodulation. To address the lack of robustness of existing time-frequency estimation algorithms under low signal-to-noise ratio (SNR) conditions and the instability of traditional methods in high-dynamic environments

this paper proposes a demodulation method for Starlink signals based on correlation and clustering

and validates it through both simulation analysis and real captured downlink signals. By exploiting the unique synchronization sequence structure of Starlink signals

a delayed autocorrelation method with enhanced correlation structure and an improved intra-symbol phase difference algorithm are designed to achieve synchronization sequence detection and coarse Doppler estimation. A symbol-by-symbol fine time-frequency estimation method based on K-means clustering is proposed. Under a non-data-aided condition

constellation clustering statistics are extracted

and the residual time-frequency errors are estimated by maximizing the empirical signal-to-noise ratio (ESNR). Simulation scenarios are constructed to analyze the convergence behavior and estimation accuracy of the proposed method. Furthermore

real captured Starlink downlink signals are employed to evaluate the algorithm performance in terms of bit error rate (BER). Experimental results demonstrate robust convergence and improved estimation accuracy under high-dynamic conditions. For real captured downlink data at 7.3 dB SNR

the proposed method reduces BER by 55.10% compared with baseline methods

enabling more reliable demodulation and supporting Starlink-based assisted navigation.

关键词

Keywords

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