Comparative analysis of UWB deconvolution and feature-extraction algorithms for GPR landmine detection
Comparative analysis of UWB deconvolution and feature-extraction algorithms for GPR landmine detection
复制标题
探地雷达地雷探测UWB反卷积与特征提取算法对比分析
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Motoyuki Sato
中科院分区：
文献类型：
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作者：
Timofei G. Savelyev;Motoyuki Sato
本文研究了超宽带探地雷达(UWB GPR)探雷目标识别算法。由于超宽带信号的非平稳性，其处理需要先进的技术，即正则化反卷积、时频或时间尺度分析。我们使用反褶积从接收到的信号中去除探地雷达和土壤特征。提出了一种基于小波噪声水平估计的正则化Wiener逆滤波反卷积的有效算法。效率的标准是去卷积后信号的稳定性、接收信号和卷积后信号之间的差异以及计算速度。该算法的创新之处在于利用小波分解进行噪声水平估计，该方法独立于信号的统计量，单独定义任何信号的噪声水平。将该算法与基于正则化的迭代时间域反卷积算法进行了比较。对于目标识别，我们将奇异值分解(SVD)应用于时频信号分布。在这里，我们比较了Wigner变换和连续小波变换(CWT)在鉴别特征选择方面的作用。用步进频率探地雷达采集的数据对所提出的算法进行了检验。
In this work we developed target recognition algorithms for landmine detection with ultra-wideband ground penetrating radar (UWB GPR). Due to non-stationarity of UWB signals their processing requires advanced techniques, namely regularized deconvolution, time-frequency or time-scale analysis. We use deconvolution to remove GPR and soil characteristics from the received signals. An efficient algorithm of deconvolution, based on a regularized Wiener inverse filter with wavelet noise level estimation, has been developed. Criteria of efficiency were stability of the signal after deconvolution, difference between the received signal and the convolved back signal, and computational speed. The novelty of the algorithm is noise level estimation with wavelet decomposition, which defines the noise level separately for any signal, independently of its statistics. The algorithm was compared with an iterative time-domain deconvolution algorithm based on regularization. For target recognition we apply singular value decomposition (SVD) to a time-frequency signal distribution. Here we compare the Wigner transform and continuous wavelet transform (CWT) for discriminant feature selection. The developed algorithms have been checked on the data acquired with a stepped-frequency GPR.