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Edge preserved extrapolation method for full polarimetrie RPM imaging with UWB radars

机译:边缘保留外推方法,用于超极化雷达的全偏振RPM成像

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Ultra-wideband (UWB) radar system has a great advantage as short range 3-dimentional(3-D) sensor with high range resolution being suitable, for target recognition in optically blurred situation. As one of the most efficient 3-D imaging approaches, the range point migration(RPM) method has been developed, which specifies target boundary extraction using measured ranges. The RPM method stills suffers from insufficient imaging area along cross-range direction, and this incurs the difficulty for target recognition using the reconstructed 3-D image, which is an essential problem in radar imaging. To tackle with this problem, an ellipsoidal aggregation based image extrapolation method, incorporating full polarimetric datasets and RPM imaging, have been already proposed. While this method retains an accurate extrapolation for smooth surface target, it suffers from inaccuracy for target shape with edge. Thus, this paper introduces an edge preserved algorithm based on eigenvector decomposition for quasi Hessian matrix obtained by RPM image. The results from finite-difference time-domain (FDTD) based simulations demonstrate that our method effectively expands a target image without sacrificing an accuracy around edge area.
机译:超宽带(UWB)雷达系统具有很大的优势,因为适合高分辨力的短距离3维(3-D)传感器适合在光学模糊情况下进行目标识别。作为最有效的3D成像方法之一,已开发了距离点偏移(RPM)方法,该方法指定了使用测量范围进行目标边界提取。 RPM方法仍然存在跨跨范围方向的成像面积不足的问题,这会导致难以使用重建的3D图像进行目标识别,这是雷达成像中的一个基本问题。为了解决这个问题,已经提出了一种基于椭球聚合的图像外推方法,该方法结合了完整的偏振数据集和RPM成像。尽管此方法为光滑的表面目标保留了精确的外推法,但对于带有边缘的目标形状却存在误差。因此,本文针对RPM图像获得的准Hessian矩阵,引入了一种基于特征向量分解的边缘保留算法。基于有限差分时域(FDTD)的仿真结果表明,我们的方法有效地扩展了目标图像,而不会牺牲边缘区域的精度。

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