Application of Polarimetric Features and Support Vector Machines for Classification of Improvised Explosive Devices
Application of Polarimetric Features and Support Vector Machines for Classification of Improvised Explosive Devices
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DOI：
10.1109/lawp.2019.2934691
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发表时间：
2019-11
影响因子：
4.2
通讯作者：
Sergio Gutierrez;F. Vega;F. González;C. Baer;J. Sachs
中科院分区：
文献类型：
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作者：
Sergio Gutierrez;F. Vega;F. González;C. Baer;J. Sachs
在这封信中，我们提出了一个二元分类问题的简易爆炸装置，真和假目标的功能是从极化探地雷达（GPR）信号。在实验室和现场的情况下，通过使用双极化Vivaldi天线和超宽带多输入多输出GPR（MIMO-GPR）系统的频率范围从0.8到5 GHz的雷达测量。递归算法，递归最小二乘，线性预测编码用于杂波去除。为了提取极化测量的目标特征，组合了八种数据处理方法，结合杂波去除算法、时频变换和奇异值分解。此外，对于每种方法，构造了13个目标特征向量。每个向量用于训练和测试分类问题的支持向量机算法。分类结果通过留二交叉验证进行验证。最佳分类器的准确率为87.02%，假阳性率为10.53%。
In this letter, we present a binary classification problem of improvised explosive devices, where true and false target features are taken from polarimetric ground-penetrating radar (GPR) signals. The radar measurements are carried out in laboratory and field scenarios by using dual-polarized Vivaldi antennas and ultrawideband multiple-input–multiple-output GPR (MIMO-GPR) systems in the frequency range from 0.8 to 5 GHz. Recursive algorithms, recursive least square, and linear predictive coding are used for clutter removal. To extract targets features of polarimetric measurements, eight data-processing methods are assembled, combining clutter removal algorithms, time–frequency transformations, and singular value decomposition. Moreover, for every method, 13 target feature vectors are constructed. Each vector is used to train and test a support vector machine algorithm for the classification problem. Classification results are validated by using leave-two-out cross-validation. Accuracy of 87.02% and a false positive rate of 10.53% in the best classifier were obtained.