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首页> 外文会议>Algorithms for Synthetic Aperture Radar Imagery XXIII >Moving Target Imaging using Sparse and Low-rank Structure
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Moving Target Imaging using Sparse and Low-rank Structure

机译：使用稀疏和低秩结构的移动目标成像

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摘要

In this paper we present a method for passive radar detection of ground moving targets using sparsely distributed apertures. We assume the scene is illuminated by a source of opportunity and measure the backscattered signal. We correlate measurements from two different receivers, then form a linear forward model that operates on a rank one, positive semi-definite (PSD) operator, formed by taking the tensor product of the phase-space reflectivity function with its self. Utilizing this structure, image formation and velocity estimation are defined in a constrained optimization framework. Additionally, image formation and velocity estimation are formulated as separate optimization problems, this results in computational savings. Position estimation is posed as a rank one PSD constrained least squares problem. Then, velocity estimation is performed as a cardinality constrained least squares problem, solved using a greedy algorithm. We demonstrate the performance of our method with numerical simulations, demonstrate improvement over back-projection imaging, and evaluate the effect of spatial diversity.
机译：在本文中，我们提出了一种使用稀疏分布孔径的无源雷达探测地面移动目标的方法。我们假设场景被机会源照亮，并测量反向散射信号。我们将来自两个不同接收器的测量值关联起来，然后形成一个线性正向模型，该模型对一阶正半定（PSD）算子进行运算，该算子是通过将相空间反射率函数的张量乘以其自身而形成的。利用这种结构，在约束优化框架中定义了图像形成和速度估计。此外，图像形成和速度估计被公式化为单独的优化问题，从而节省了计算量。位置估计被认为是一阶PSD约束的最小二乘问题。然后，将速度估计作为基数约束的最小二乘问题执行，并使用贪婪算法求解。我们通过数值模拟证明了我们方法的性能，证明了背投影成像技术的改进，并评估了空间多样性的影响。

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  • 会议地点 Baltimore MD(US)
  • 作者

    Eric Mason; Birsen Yazici;

  • 作者单位

    Department of Electrical, Computer and System Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA;

    Department of Electrical, Computer and System Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA;

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  • 正文语种 eng
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