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基于STFT谱图滑窗相消的微动杂波去除方法
引用本文:万显荣,谢德强,易建新,胡仕波,童云. 基于STFT谱图滑窗相消的微动杂波去除方法[J]. 雷达学报, 2022, 11(5): 794-804. DOI: 10.12000/JR22157
作者姓名:万显荣  谢德强  易建新  胡仕波  童云
作者单位:武汉大学电子信息学院 武汉 430072
基金项目:国家自然科学基金(61931015, 62071335),湖北省技术创新专项重大项目(2019AAA061),湖北省自然科学基金创新群体项目(2021CFA002)
摘    要:微动杂波往往具有较大的多普勒展宽,会抬高噪底、湮没弱目标,造成虚警和漏检。有效去除微动杂波对提高雷达性能具有重要意义。该文利用匀速目标回波和微动杂波在短时傅里叶变换(STFT)谱图中的形态差异,提出了一种基于STFT谱图滑窗相消的微动杂波去除方法。具体地,匀速运动目标回波在STFT谱图中表现为特定频率单元上平行于时间轴的直线型能量条带,而微动杂波具有时变非平稳特性,在STFT谱图中呈现出横跨多个频率单元的时变复杂形态。将原始STFT谱图沿时间维滑窗得到新的STFT谱图,则目标回波分布在这两种谱图中的相同位置,而微动杂波在这两种谱图中的位置存在明显差异。因此将上述两种谱图相减,根据相减前后谱图中各单元的强度变化情况,即可将目标回波和微动杂波分离,达到去除微动杂波的效果。仿真和实测结果均验证了所提方法的有效性。与常见基于时频变换的L-statistics算法相比,所提方法能够在去除微动杂波的同时,较好地保留了目标回波。

关 键 词:微多普勒效应  微动杂波  短时傅里叶变换  短时傅里叶变换谱图
收稿时间:2022-07-21

Micro-Doppler Clutter Removal Method Based on the Cancelation of Sliding STFT Spectrogram
WAN Xianrong,XIE Deqiang,YI Jianxin,HU Shibo,TONG Yun. Micro-Doppler Clutter Removal Method Based on the Cancelation of Sliding STFT Spectrogram[J]. Journal of Radars, 2022, 11(5): 794-804. DOI: 10.12000/JR22157
Authors:WAN Xianrong  XIE Deqiang  YI Jianxin  HU Shibo  TONG Yun
Affiliation:School of Electronic Information, Wuhan University, Wuhan 430072, China
Abstract:Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo. 
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