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遥感技术与应用  2020, Vol. 35 Issue (3): 645-655    DOI: 10.11873/j.issn.1004-0323.2020.3.0645
数据与图像处理     
基于EMD-RSPWVD算法的SAR目标运动参数仿真分析与应用研究
潘方博1,2(),陈锟山1()
1.中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101
2.中国科学院大学,北京 100049
Simulation Analysis and Application of SAR Target Motion Parameters based on EMD-RSPWVD Algorithm
Fangbo Pan1,2(),Kunshan Chen1()
1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences, Beijing 100101, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
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摘要:

针对传统时频分析方法处理多分量SAR运动目标回波数据时出现的交叉项影响严重和时频聚集性差等问题,提出一种融合改进的经验模式分解(Empirical Mode Decomposition, EMD)算法和重排平滑伪维格纳维尔分布(Reassigned Smoothing Pseudo-Wigner-Ville Distribution, RSPWVD)算法的新时频分析算法——EMD-RSPWVD算法。利用改进的EMD算法将多分量SAR动目标回波信号分解为彼此独立信号分量,然后对独立分量分别做基于RSPWVD算法的时频分析,以消除交叉项和获得高的时间—频率分辨率。分别利用模拟回波信号数据和真实回波信号数据,探究该算法对于多分量SAR运动回波数据的分析性能。结果表明,该算法具有良好的抗噪性和运动目标检测能力,以及高精度的运动参数估计性能。

关键词: EMD算法RSPWVD算法运动目标运动参数    
Abstract:

When processing multi-component SAR moving target echo data by traditional time-frequency analysis method, there is serious cross-term influence and poor time-frequency clustering. A new time-frequency analysis algorithm named EMD-RSPWVD is proposed. It combines the improved Empirical Mode Decomposition (EMD) algorithm and Reassigned Smoothing Pseudo-Wigner-Ville Distribution (RSPWVD) algorithm. The improved EMD algorithm is used to decompose the multi-component SAR moving target echo signal into independent signal components. Then the time-frequency analysis of independent components which based on RSPWVD algorithm is performed to eliminate cross-terms and obtain high time-frequency resolution. Finally, simulated echo data and real echo data are used to analyze the performance of this algorithm for multi-component SAR motion echo data. The results show that the algorithm has good anti-noise ability, moving target detection ability and high-precision motion parameter estimation performance.

Key words: EMD algorithm    RSPWVD algorithm    Moving target    Motion parameter
收稿日期: 2019-04-21 出版日期: 2020-07-10
ZTFLH:  TP75  
基金资助: 国家自然科学基金重点项目“同轨集合SAR-Scatterometer的新型微波多维探测理论与模式研究”(41531175)
通讯作者: 陈锟山     E-mail: panfb@radi.ac.cn;chenks@radi.ac.cn
作者简介: 潘方博(1992-),男,河南开封人,主要从事SAR信号分析与应用研究。E-mail:panfb@radi.ac.cn
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引用本文:

潘方博,陈锟山. 基于EMD-RSPWVD算法的SAR目标运动参数仿真分析与应用研究[J]. 遥感技术与应用, 2020, 35(3): 645-655.

Fangbo Pan,Kunshan Chen. Simulation Analysis and Application of SAR Target Motion Parameters based on EMD-RSPWVD Algorithm. Remote Sensing Technology and Application, 2020, 35(3): 645-655.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.3.0645        http://www.rsta.ac.cn/CN/Y2020/V35/I3/645

图1  SAR场景示意图
图2  动目标检测及运动参数估计流程图
图3  EMD-RSPWVD算法流程图
图4  3个运动目标在场景1中不同时刻位置示意图
参数名称
雷达速度/(m/s)150
脉冲持续时间/μs2
调频斜率/(Hz/s)2e13
距离向采样率/MHz60
PRF/Hz160
多普勒带宽/Hz80
雷达中心频率/GHz5.3
雷达与目标最短距离/m20 000
表1  SAR参数表
图5  不同时频分析方法处理结果
图6  地面运动目标物数量为2的4个场景的SAR回波数据时频分析结果图
图7  地面移动目标物数量为3和4的6个场景的时频分析结果图
图8  SAR场景下两动目标位置示意图
图9  两个运动目标EMD-RSPWVD时频分析结果图(方位线编号512、514)
目标理论值估计值估计误差/%
fdcfdrf?dcf?drfdc-f?dcfdcfdr-f?drfdr
101.280 901.235 103.575 6
217.6692.473 517.585 62.508 80.446 61.427 1
表2  运动参数估计结果
图10  EMD-RSPWVD算法抗噪性分析
系统参数
雷达速度/(m/s)7 062
载频/GHz5.3
距离向带宽/MHz30.12
PRF/Hz1 256.98
距离向采样频率/MHz32.32
脉冲持续时间/μs41.74
多普勒中心频率/Hz-6 900
方位向调频率/(Hz/s)1 733
表3  Radarsat-1数据系统参数
图11  Radarsat-1真实数据RDA成像结果及时频分析相关结果图
参考值本文结果误差/%
fdcfdrf?dcf?drfdc-f?dcfdcfdr-f?drfdr
347.013 21773.020 5334.739 11778.201 53.537 10.292 2
表4  Radarsat-1真实数据运动参数估计结果
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