系统工程与电子技术 ›› 2023, Vol. 45 ›› Issue (2): 343-351.doi: 10.12305/j.issn.1001-506X.2023.02.03

• 电子技术 • 上一篇    

基于可调Q因子小波变换的海杂波抑制算法

张俊玲, 董玫, 陈伯孝   

  1. 西安电子科技大学雷达信号处理国家重点实验室, 陕西 西安 710071
  • 收稿日期:2021-09-28 出版日期:2023-01-13 发布日期:2023-02-04
  • 通讯作者: 董玫
  • 作者简介:张俊玲(1997—), 女, 硕士研究生, 主要研究方向为海杂波信号处理
    董玫(1980—), 女, 副教授, 博士,主要研究方向为雷达信号处理
    陈伯孝(1966—), 男, 教授, 博士,主要研究方向为雷达信号处理
  • 基金资助:
    国家自然科学基金(6971323);国防科技基础加强计划(2019-JCJQ-ZD-067-00)

Sea clutter suppression algorithm based on tunable Q-factor wavelet transform

Junling ZHANG, Mei DONG, Baixiao CHEN   

  1. National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
  • Received:2021-09-28 Online:2023-01-13 Published:2023-02-04
  • Contact: Mei DONG

摘要:

针对海杂波背景下弱目标检测中存在的信杂比低的问题, 提出了改进的基于可调Q因子小波变换的海杂波抑制算法。由于海杂波能量远大于目标信号能量, 提出选取与海杂波振荡特性相匹配的参数进行可调Q因子小波变换, 得到各小波子带的系数, 并对小波系数进行稀疏优化后重构海杂波信号。为了判断弱目标信号是否存在, 提出一种自适应的阈值检测方法, 将原始回波信号与海杂波重构信号的差作为检测样本, 实现对弱目标信号的检测。该算法不依赖海杂波具体模型。最后对某实测海杂波数据集进行实验, 验证了所提算法的正确性。

关键词: 海杂波抑制, 可调Q因子小波变换, 稀疏优化, 自适应阈值选择

Abstract:

For the problem of low signal to clutter ratio in weak target detection under sea clutter, an improved sea clutter suppression algorithm based on tunable Q-factor wavelet transform is proposed. Since the energy of the sea clutter is much greater than the energy of the target, selecting parameters which match the characteristics of the sea clutter oscillation is proposed to conduct tunable Q-factor wavelet transform and obtain the coefficients of each wavelet sub-band. The wavelet coefficients are sparsely optimized for reconstructing the sea clutter. In order to judge whether the weak target signal exists, an adaptive threshold detection method is proposed. It uses the difference between the original echo signal and the reconstructed sea clutter as the detection sample to detect the weak target. The algorithm does not rely on specific models of sea clutter. Finally, the experimental results on a measured sea clutter data set show that the proposed algorithm is correct.

Key words: sea clutter suppression, tunable Q-factor wavelet transform(TQWD), sparse optimization, adaptive threshold selection

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