基于STFT谱图滑窗相消的微动杂波去除方法

万显荣; 谢德强; 易建新; 胡仕波; 童云

doi:10.12000/JR22157

基于STFT谱图滑窗相消的微动杂波去除方法

DOI: 10.12000/JR22157

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)

详细信息

作者简介:
万显荣，博士，教授，博士生导师，研究方向为新体制雷达设计，如外辐射源雷达、高频超视距雷达系统及信号处理

谢德强，博士生，研究方向为外辐射源雷达系统设计及信号处理、稀疏雷达信号处理、GPU并行计算与实时信号处理等

易建新，博士，副教授，硕士生导师，研究方向为外辐射源雷达信号处理、目标跟踪和信息融合

胡仕波，博士生，研究方向为外辐射源雷达系统设计和雷达信号处理等

童　云，博士生，研究方向为外辐射源雷达系统与信号处理、雷达信号检测等

通讯作者:
万显荣 xrwan@whu.edu.cn

责任主编：张群 Corresponding Editor: ZHANG Qun
中图分类号: TN958
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出版历程
- 收稿日期: 2022-07-21
- 修回日期: 2022-09-09
- 网络出版日期: 2022-09-29
- 刊出日期: 2022-10-28

Micro-Doppler Clutter Removal Method Based on the Cancelation of Sliding STFT Spectrogram

School of Electronic Information, Wuhan University, Wuhan 430072, China

Funds: The National Natural Science Foundation of China (61931015, 62071335), The Technological Innovation Project of Hubei Province of China (2019AAA061), The Innovation Group Project of Natural Science Foundation of Hubei Province (2021CFA002)

More Information

Corresponding author: WAN Xianrong, xrwan@whu.edu.cn

摘要

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。
- 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
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.
- Micro-Doppler effect /
- Micromotion clutter /
- Short-Time Fourier Transform (STFT) /
- STFT spectrogram

HTML全文

图 1 Hamming窗$R = {N_w}/2$时的COLA验证

Figure 1. Validation of the COLA compliant for Hamming window when $R = {N_w}/2$

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图 2 所提算法基本原理示意图

Figure 2. Diagram of the basic principle of the proposed method

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图 3 滑窗示意图

Figure 3. Diagram of the sliding window

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图 4 STFT谱滑窗相消算法流程图

Figure 4. Diagram of the STFT-SSC method

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图 5 多散射点微动杂波仿真信号的频谱和STFT谱图

Figure 5. Spectrum and STFT spectrogram of the simulated signal with multiple scattering points micro-motion clutter

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图 6 STFT-SSC和L-statistics算法多散射点微动杂波处理结果对比

Figure 6. Comparison of the processing results between the STFT-SSC and L-statistics for multi-scattering points micro-motion clutter

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图 7 旋转叶片微动杂波仿真信号的频谱和STFT谱图

Figure 7. Spectrum and STFT spectrogram of the rotating blade micro-motion clutter simulation signal

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图 8 STFT-SSC和L-statistics算法旋转叶片微动杂波处理结果对比

Figure 8. Comparison of the processing results between the STFT-SSC and L-statistics for rotating blade micro-motion clutter

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图 9 实验场景图

Figure 9. Experimental scene

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图 10 原始信号频谱和STFT谱图

Figure 10. Spectrum and STFT spectrogram of the original signal

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图 11 STFT-SSC和L-statistics实测处理结果对比

Figure 11. Comparison of the processing results of field experimental data between the STFT-SSC and L-statistics

下载: 全尺寸图片幻灯片

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