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

机译:UWB雷达完整偏光型RPM成像的边缘保存的外推方法

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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,与短距离的三维(3-D)传感器具有高范围分辨率,适用于光学模糊情况下的目标识别。作为最有效的3-D成像方法之一,已经开发了范围迁移(RPM)方法,其使用测量范围指定目标边界提取。 RPM方法仍然存在沿跨射程方向的成像区域不足,并且这种使用重建的3-D图像难以实现目标识别,这是雷达成像中的重要问题。为了解决这个问题,已经提出了一种基于椭圆形聚集的图像推断方法,包括完全偏振数据集和RPM成像。虽然该方法保留了光滑表面目标的精确外推,但它遭受了与边缘的目标形状的不准确性。因此,本文介绍了基于rpm图像获得的准Hessian矩阵的特征向量分解的边缘保存算法。基于有限差分时域(FDTD)模拟的结果表明,我们的方法有效地扩展目标图像而不牺牲边缘区域的精度。

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