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Moving Target Detection Based on the Spreading Characteristics of SAR Interferograms in the Magnitude-Phase Plane

机译:基于幅度相平面中SAR干涉图扩展特性的运动目标检测

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摘要

We propose a constant false alarm rate (CFAR) algorithm for moving target detection in synthetic aperture radar (SAR) images based on the spreading characteristics of interferograms on the magnitude-phase (M-P) plane. This method is based on the observation that, in practice, both moving and stationary targets along with clutter are located at different regions in the M-P plane, and hence reasonable partitions of the M-P plane can help in detecting moving targets. To ensure efficient CFAR detection and to resolve the effect of factors that influence detection results, the proposed algorithm is divided into three distinct stages: coarse detection, fine detection, and post-processing. First, to accurately describe the statistical behavior of clutter, a global censoring strategy, called coarse detection, is introduced to adaptively eliminate the influences of the moving and stationary target points from the given data. Then, to acquire fine detection results, a novel CFAR detector is developed on the basis of the fits of a known theoretical M-P joint probability density function (PDF) against the two-dimensional (2-D) histogram of the censored clutter. The joint PDF’s projected contour line that satisfies the desirable probability of false alarm (PFA) corresponds to the required threshold of detection in the M-P plane. Finally, two filters, the magnitude and phase filters, are applied to reduce the false alarms generated from the previous procedures. The effectiveness of the proposed algorithm is validated through experimental results obtained from a two-channel SAR complex image.
机译:我们基于干涉图在幅相(M-P)平面上的扩展特性,提出了用于合成孔径雷达(SAR)图像中运动目标检测的恒定误报率(CFAR)算法。该方法基于以下观察结果:实际上,运动目标和静止目标以及杂波都位于M-P平面的不同区域,因此,M-P平面的合理划分可以帮助检测运动目标。为了确保有效的CFAR检测并解决影响检测结果的因素的影响,该算法分为三个不同的阶段:粗略检测,精细检测和后处理。首先,为了准确描述杂波的统计行为,引入了一种称为粗略检测的全局检查策略,以从给定数据中自适应地消除移动和静止目标点的影响。然后,为了获得良好的检测结果,根据已知的理论M-P联合概率密度函数(PDF)对受检杂波的二维(2-D)直方图的拟合,开发了新型CFAR检测器。满足期望的虚警概率(PFA)的联合PDF投影轮廓线对应于M-P平面中所需的检测阈值。最后,应用了两个滤波器,即幅度和相位滤波器,以减少由先前过程产生的误报。通过从两通道SAR复杂图像获得的实验结果验证了该算法的有效性。

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