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基于天波重构技术的eLORAN信号周期识别算法

刘时尧 华宇 张首刚

刘时尧, 华宇, 张首刚. 基于天波重构技术的eLORAN信号周期识别算法[J]. 电子与信息学报, 2022, 44(10): 3592-3601. doi: 10.11999/JEIT210661
引用本文: 刘时尧, 华宇, 张首刚. 基于天波重构技术的eLORAN信号周期识别算法[J]. 电子与信息学报, 2022, 44(10): 3592-3601. doi: 10.11999/JEIT210661
LIU Shiyao, HUA Yu, ZHANG Shougang. A Cycle Identification Algorithm for enhanced LOng RAnge Navigation Signal Based on Skywave Reconstruction Technology[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3592-3601. doi: 10.11999/JEIT210661
Citation: LIU Shiyao, HUA Yu, ZHANG Shougang. A Cycle Identification Algorithm for enhanced LOng RAnge Navigation Signal Based on Skywave Reconstruction Technology[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3592-3601. doi: 10.11999/JEIT210661

基于天波重构技术的eLORAN信号周期识别算法

doi: 10.11999/JEIT210661
基金项目: 国家自然科学基金(11803040),中国科学院前沿科学重点研究项目(QYZDJ-SSW-JSC034)
详细信息
    作者简介:

    刘时尧:男,博士生,研究方向为无线电导航与授时技术

    华宇:男,博士,研究员,研究方向为无线电导航与授时技术

    张首刚:男,博士,研究员,研究方向为原子钟及其导航应用技术

    通讯作者:

    刘时尧 liushiyao@ntsc.ac.cn

  • 中图分类号: TN961

A Cycle Identification Algorithm for enhanced LOng RAnge Navigation Signal Based on Skywave Reconstruction Technology

Funds: The National Natural Science Foundation of China (11803040), The Key Research Program of Frontier Sciences of CAS (QYZDJ-SSW-JSC034)
  • 摘要: 针对增强型罗兰(eLORAN)系统在信号处理中的核心问题——周期识别,该文提出一种应对强天波干扰及低信噪比(SNR)等恶劣环境的联合算法。该方法首先在改进窗函数的基础上利用频谱相除技术估计信号特征参数,并根据大数理论的思想实现了天地波识别;其次,提出天地波时延差及幅度比的自适应2阶网格搜索匹配算法,在节省计算量的同时准确重构并去除天波;最后利用伪地波信号准确实现周期识别。仿真结果分析表明,该算法能够成功地克服现有技术中的一些弊端,实现小时延差及大强度天波干扰下的天波识别及分离,同时结合频谱相除技术的稳定性极大提高周期识别的正确率,进而为后续解调解码等流程提供保障。
  • 图  1  eLORAN脉冲波形

    图  2  联合算法总体框图

    图  3  改进窗函数及效果图

    图  4  天地波分离模块流程图

    图  5  天波分离处理前后信号对比

    图  6  利用最优伪地波进行标准过0点估计示意图

    图  7  天波位置估计误差均方根

    图  8  天波幅度估计误差率均方根

    表  1  周期识别错误次数及准确率(SGR=0 dB)

    SNR(dB)$\Delta $τ (μs)准确率(%)
    384042444648505254565860
    0621400000000099.74
    216000000000099.99
    4000000000000100.00
    6000000000000100.00
    8000000000000100.00
    10000000000000100.00
    下载: 导出CSV

    表  2  天地波分离后周期识别错误次数及准确率(SGR=18 dB)

    SNR(dB)$\Delta $τ (μs)准确率(%)
    384042444648505254565860
    0199413161115801799.22
    21030107060100399.67
    420011000000099.97
    600002000000099.98
    8000000000000100.00
    10000000000000100.00
    下载: 导出CSV

    表  3  天地波分离后周期识别错误次数及准确率(SGR=24 dB)

    SNR(dB)$\Delta $τ (μs)准确率(%)
    384042444648505254565860
    011722511225362901298.89
    2641182080160699.57
    400000050030099.93
    6000000000000100.00
    8000000000000100.00
    10000000000000100.00
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-02
  • 修回日期:  2021-12-31
  • 录用日期:  2022-01-05
  • 网络出版日期:  2022-02-01
  • 刊出日期:  2022-10-19

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