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Automated Acquisition and Processing of 3D GPR Data for Object Detection and Characterization

机译:自动采集和处理3D GPR数据以进行目标检测和表征

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摘要

To improve image resolution of GPR data, proper data acquisition is the first key part of the whole imaging sequence. Obtainable down and cross range resolutions, insofar as they are related to acquisition, are determined by the frequency bandwidth of the GPR equipment, the temporal and spatial sampling rates and the accurate knowledge of the antenna positions. The key parameters for three-dimensional imaging algorithms are wave velocity distribution in the domain of interest, radiation (polarization and amplitude) characteristics of the antenna system and emitted waveform. To this end we have designed and manufactured a measurement frame with high horizontal position accuracy of the antennas, by using a servomotor that automatically moves the antennas over the whole area of the measurement frame for a fixed vertical position of the antennas. When acquisition is completed, the data needs to be processed such that object detection and characterization is most easily accomplished. In most cases, object detection can be carried out on the preprocessed data itself, but in some cases and certainly for characterization purposes improved image resolution is required. This is realized using imaging algorithms, preferably those that take into account the polarization and amplitude characteristics of the antennas.
机译:为了提高GPR数据的图像分辨率,正确的数据采集是整个成像序列的第一关键部分。在与采集有关的范围内,可获得的向下和跨范围分辨率取决于GPR设备的频率带宽,时间和空间采样率以及对天线位置的准确了解。三维成像算法的关键参数是感兴趣域中的波速分布,天线系统的辐射(极化和幅度)特性以及发射波形。为此,我们通过使用伺服电机设计并制造出了具有高水平天线定位精度的测量框架,该伺服电动机可自动将天线在测量框架的整个区域内移动,以实现天线的固定垂直位置。采集完成后,需要对数据进行处理,以便最轻松地完成对象检测和表征。在大多数情况下,可以对预处理后的数据本身进行对象检测,但是在某些情况下,当然也出于表征的目的,需要提高图像分辨率。这是使用成像算法来实现的,最好是考虑到天线的极化和幅度特性的成像算法。

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