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基于Kalman滤波的水声混合双向迭代信道均衡算法

杨斌斌 鄢社锋 章绍晨 叶子豪

杨斌斌, 鄢社锋, 章绍晨, 叶子豪. 基于Kalman滤波的水声混合双向迭代信道均衡算法[J]. 电子与信息学报, 2022, 44(6): 1879-1886. doi: 10.11999/JEIT211343
引用本文: 杨斌斌, 鄢社锋, 章绍晨, 叶子豪. 基于Kalman滤波的水声混合双向迭代信道均衡算法[J]. 电子与信息学报, 2022, 44(6): 1879-1886. doi: 10.11999/JEIT211343
YANG Binbin, YAN Shefeng, ZHANG Shaochen, YE Zihao. Hybrid Bi-directional Turbo Equalization for Underwater Acoustic Communications Based on Kalman Filter[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1879-1886. doi: 10.11999/JEIT211343
Citation: YANG Binbin, YAN Shefeng, ZHANG Shaochen, YE Zihao. Hybrid Bi-directional Turbo Equalization for Underwater Acoustic Communications Based on Kalman Filter[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1879-1886. doi: 10.11999/JEIT211343

基于Kalman滤波的水声混合双向迭代信道均衡算法

doi: 10.11999/JEIT211343
基金项目: 国家自然科学基金(61725106)
详细信息
    作者简介:

    杨斌斌:男,1994年生,博士生,研究方向为水声信道估计与信道均衡

    鄢社锋:男,1978年生,教授,研究方向为阵列信号处理与水声通信等

    章绍晨:男,1996年生,硕士生,研究方向为水声信道估计与信道编码

    叶子豪:男,1996年生,博士生,研究方向为水声信道估计与信道均衡

    通讯作者:

    鄢社锋 sfyan@ieee.org

  • 中图分类号: TN929.3

Hybrid Bi-directional Turbo Equalization for Underwater Acoustic Communications Based on Kalman Filter

Funds: The National Natural Science Foundation of China (61725106)
  • 摘要: 水声信道均衡中基于信道估计的均衡方法理论上具有更优的均衡性能,但较高的计算复杂度限制了算法的实际应用。针对这一问题,该文首先基于Kalman滤波和Turbo均衡提出一种迭代Kalman均衡器,实现了基于软符号的迭代信道估计与迭代Kalman均衡,且复杂度较常规方法降低约1个数量级。其次,针对单一均衡算法和单一方向Turbo均衡器存在的误差传递现象,设计了基于迭代Kalman均衡器与改进成比例归一化LMS (IPNLMS)自适应均衡器相结合的混合双向Turbo均衡器,提高了自适应均衡器的收敛速度和均衡性能,并通过双向均衡结构带来的增益改善了符号估计误差传递的现象。理论分析与仿真实验验证了该文算法的有效性。
  • 图  1  基于软信息迭代的Kalman-Turbo均衡系统

    图  2  基于Kalman滤波的水声混合双向迭代信道均衡算法

    图  3  仿真信道时域冲激响应

    图  4  信道估计算法复杂度对比

    图  5  第2次迭代解调结果

    图  6  各算法均衡误码率与信噪比关系

    图  7  各算法译码误码率与信噪比关系

    表  1  仿真系统实验参数设置

    参数参数值
    采样频率96 kHz
    中心频率12 kHz
    信号带宽6 kHz
    符号速率6×103 symbols/s
    调制方式QPSK
    编码方式CONV
    编码码率0.5
    训练数据长度200 symbols
    下载: 导出CSV

    表  2  均衡器算法复杂度比较

    均衡器计算复杂度
    SIC$ \mathcal{O}(5N{\text{ + 3}}) $
    Bi-SIC$ \mathcal{O}(10N{\text{ + 6}}) $
    HSIC$ \mathcal{O}({N^3}) $
    Bi-HSIC$ \mathcal{O}({N^3} + 5N + 3) $
    迭代Kalman$ \mathcal{O}(4{N^2} + 4N + 1) $
    HBi-KEQ$ \mathcal{O}(4{N^2} + 9N + 4) $
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-29
  • 修回日期:  2022-04-26
  • 录用日期:  2022-04-30
  • 网络出版日期:  2022-05-06
  • 刊出日期:  2022-06-21

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