上海交通大学学报

上海交通大学学报 >

2022 , Vol. 56 >Issue 2: 231 - 241

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.432

高质量LMSCT时频分析算法及其在雷达信号目标检测中的应用

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  • 1. 中国地质大学(武汉) 机械与电子信息学院,武汉 430074
    2. 杜克大学 数学系,美国Durham 27708
    3. 中国地质大学(武汉) 复杂系统先进控制与智能自动化湖北重点实验室, 武汉 430074
郝国成(1975-),男,山东省聊城市人,副教授,博士生导师,主要从事非平稳信号时频分析算法研究.

收稿日期: 2020-12-19

  网络出版日期: 2022-03-03

基金资助

武汉市科技局攻关项目(2016060101010073);高等学校学科创新引智计划(B17040);大地测量与地球动力学国家重点实验室开放基金(SKLGED2018-5-4-E);复杂系统先进控制与智能自动化湖北省重点实验室基金(ACIA2017002);智能地学信息处理湖北省重点实验室开放课题资助项目(KLIGIP2017A01)

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A High Quality Algorithm of Time-Frequency Analysis and Its Application in Radar Signal Target Detection via LMSCT

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  • 1.School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
    2.Department of Mathematics, Duke University, Durham 27708, USA
    3.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China

Received date: 2020-12-19

  Online published: 2022-03-03

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摘要

针对线性调频小波变换(CT)引入的调频率参数不能完全匹配信号的瞬时频率及算法抗噪性能不佳等问题,提出高质量的局部最大值同步压缩线调频小波变换 (LMSCT) 算法,改善CT时频分布图能量扩散幅度出现的偏差.所提算法的核心思想是通过局部最大值同步压缩操作重新分配CT的频率点.实验结果表明,LMSCT算法具有较高的时频聚集度,并且能够较好地抑制噪声的干扰,在低信噪比的情况下仍然保持良好的时频聚集度.在IPIX处理雷达信号分析中,LMSCT 算法能够较为清晰地描绘目标信号的时间-频率联合分布特性,并且确定目标出现的距离单元,为海杂波背景下的IPIX雷达信号小目标检测提供判断依据.

关键词： 时频分析; 线调频小波变换; 局部最大值同步压缩变换; IPIX雷达信号

本文引用格式

郝国成, 张必超, 锅娟, 张雅冰, 石光耀, 王盼盼, 张薇 . 高质量LMSCT时频分析算法及其在雷达信号目标检测中的应用[J]. 上海交通大学学报, 2022 , 56(2) : 231 -241 . DOI: 10.16183/j.cnki.jsjtu.2020.432

Abstract

Aimed at the fact that the chirplet rate parameter of the chirplet transform (CT) cannot match the instantaneous frequency of the signal completely, and that the anti-noise performance of the algorithm is poor, this paper proposes a high-quality local maximum synchrosqueezing chirplet transform (LMSCT) algorithm to improve the deviation of energy diffusion amplitude in CT time-frequency (TF)distribution. The main idea of this algorithm is to reallocate CT frequency points by local maximum synchrosqueezing operation. The experiment results show that the LMSCT algorithm has a higher TF concentration and a strong ability to suppress the interference of noise. The method can maintain a better resolution of TF representation at a low signal-to-noise ratio. In the application analysis of IPIX processing radar signals, the LMSCT algorithm can clearly describe the TF joint distribution characteristic of target signal and determine the distance unit of target, which provides the judgement basis for small target detection of IPIX radar signal in the background of sea clutter.

Key words： time-frequency (TF) analysis; chirplet transform (CT); local maximum synchrosqueezing transform; IPIX processing radar signal

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