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首页> 外文期刊>Remote sensing letters >Estimation of the velocity of a moving ground target using a SAR System, based on a modified multiple-measurement vector model
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Estimation of the velocity of a moving ground target using a SAR System, based on a modified multiple-measurement vector model

机译:基于改进的多次测量矢量模型,使用SAR系统估算移动地面目标的速度

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摘要

A velocity estimation method of ground moving target with sparse sampling data is proposed in this letter. Firstly, the moving target echo signal is converted to squinted synthetic aperture radar (SAR) received signal. Then, a modified multiple-measurement vector (MMV) model is built to obtain the sparse representation of ground moving target, and the improved orthogonal matching pursuit (OMP) algorithm (range migration OMP, RM-OMP) is utilized to reconstruct the coarse image of moving target. Finally, the azimuth-and range-velocity of moving target can be estimated simultaneously by searching all the possible velocity such that the minimum-entropy of coarse image is reached. Different from the homologous velocity estimation methods, the proposed method can obtain accurate estimation with signals sampled below the Nyquist rate. And benefits from the multiple measurements, the proposed method can produce better estimation accuracy and less computational complexity than the single measurement vector (SMV)-based method. The simulated data processing results validate the effectiveness of the proposed method.
机译:本文提出了一种基于稀疏采样数据的地面运动目标速度估计方法。首先,将运动目标回波信号转换为斜视合成孔径雷达(SAR)接收信号。然后,建立改进的多次测量矢量(MMV)模型以获得地面运动目标的稀疏表示,并利用改进的正交匹配追踪(OMP)算法(距离偏移OMP,RM-OMP)重建粗略图像移动目标。最后,通过搜索所有可能的速度可以同时估算运动目标的方位角和范围速度,从而达到粗糙图像的最小熵。与同源速度估计方法不同,所提出的方法可以在奈奎斯特速率以下采样的信号下获得准确的估计。得益于多次测量,与基于单测量矢量(SMV)的方法相比,该方法可产生更好的估计精度和更低的计算复杂度。仿真数据处理结果验证了该方法的有效性。

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  • 来源
    《Remote sensing letters》 |2017年第12期|937-946|共10页
  • 作者单位

    Air Force Engn Univ, Sch Informat & Nav, Xian, Peoples R China;

    Air Force Engn Univ, Sch Informat & Nav, Xian, Peoples R China;

    Air Force Engn Univ, Sch Informat & Nav, Xian, Peoples R China;

    Air Force Engn Univ, Sch Informat & Nav, Xian, Peoples R China;

    Air Force Engn Univ, Sch Informat & Nav, Xian, Peoples R China;

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