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基于似然函数的双曲调频信号参数估计快速算法

马碧云 元达鹏 刘娇蛟

马碧云, 元达鹏, 刘娇蛟. 基于似然函数的双曲调频信号参数估计快速算法[J]. 电子与信息学报, 2021, 43(5): 1228-1234. doi: 10.11999/JEIT200044
引用本文: 马碧云, 元达鹏, 刘娇蛟. 基于似然函数的双曲调频信号参数估计快速算法[J]. 电子与信息学报, 2021, 43(5): 1228-1234. doi: 10.11999/JEIT200044
Biyun MA, Dapeng YUAN, Jiaojiao LIU. Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function[J]. Journal of Electronics and Information Technology, 2021, 43(5): 1228-1234. doi: 10.11999/JEIT200044
Citation: Biyun MA, Dapeng YUAN, Jiaojiao LIU. Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function[J]. Journal of Electronics and Information Technology, 2021, 43(5): 1228-1234. doi: 10.11999/JEIT200044

基于似然函数的双曲调频信号参数估计快速算法

doi: 10.11999/JEIT200044
基金项目: 广东省自然资源厅广东省海洋经济发展专项基金(粤自然资合[2020]009号),国家自然科学基金(61302056,61401158),华南理工大学中央高校基本科研业务费专项资金(2017MS047)
详细信息
    作者简介:

    马碧云:女,1982年生,副教授,主要研究方向为超声检测、无线携能通信研究

    通讯作者:

    刘娇蛟 jjliu@scut.edu.cn

  • 中图分类号: TN911.7

Fast Algorithm for Parameter Estimation of Hyperbolic Frequency Modulation Signals Based on Likelihood Function

Funds: The Key Program of Marine Economy Development, Department of Natural Resource of Guangdong Province under Grant (YZRZH[2020]009), The National Natural Science Foundation of China (61302056, 61401158), The Basic Research Business Expenses of Central Universities of South China University of Technology (2017MS047)
  • 摘要: 相较于线性调频(LFM)信号,双曲调频(HFM)信号因具有良好的脉冲压缩性能和多普勒不变性,被广泛用于雷达侦查、水声探测等多普勒影响严重的场景中,其中HFM信号的参数估计问题尤为重要。有鉴于此,该文提出一种基于似然函数的HFM信号参数估计快速算法。文中首先推导出HFM信号的Cramer-Rao下界作为参数估计的性能评估标准;然后基于高斯随机噪声,构建了HFM信号的似然函数,并结合数据向量化的特点提出一种改进的适应度函数,最后利用全局最优引导人工蜂群(GABC)算法对该适应度函数进行极值寻优,从而实现HFM信号的参数估计;通过蒙特卡洛仿真证明了该方法在信噪比为3 dB以上时,HFM信号的参数估计结果的均方误差更逼近Cramer-Rao下界,且运算量约是原来的1/3,在保证估计精度的同时提高算法收敛速度。
  • 图  1  不同适应度函数的2维遍历结果

    图  2  不同方法下估计参数的均方误差与Cramer-Rao下界

    图  3  基于不同适应度函数的GABC算法收敛速度

    表  1  不同方法的时间复杂度

    方法时间复杂度
    最大似然估计$O\left( {\displaystyle\prod\nolimits_{j = 1}^D {\frac{ {\left( { {u_j} - {l_j} } \right)} }{ { {p_j} } } } } \right)$
    GA算法$O\left( {({C_{{\rm{GA}}}} - 2)({N_{{\rm{GA}}}} + 1){T_{{\rm{fit}}}}} \right)$
    ABC算法$O\left( {{C_{{\rm{ABC}}}}{N_{{\rm{ABC}}}}{T_{{\rm{fit}}}}} \right)$
    GABC算法$O\left( {{C_{{\rm{GABC}}}}{N_{{\rm{GABC}}}}{T_{{\rm{fit}}}}} \right)$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-01-13
  • 修回日期:  2020-08-01
  • 网络出版日期:  2020-08-24
  • 刊出日期:  2021-05-18

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