3.8 Proceedings Paper

Through-the-Wall Target Detection Using GPR A-Scan Data: Effects of Different Wall Structures on Detection Performance

出版社

IEEE

关键词

Ground penetrating radar (GPR); through-the-wall target detection (TTW); ultra-wideband sensing; feature extraction; preprocessing; cumulative energy curves

资金

  1. METU-BAP-1 Scientific Research Projects Funds [BAP-03-01-2016-005]

向作者/读者索取更多资源

Ground penetrating radar (GPR) is an electromagnetic sensor based on the ultra-wideband radar technology that can also be used for through-the-wall (TTW) target recognition. Search for the presence of designated targets hidden behind the walls, such as stationary or moving human bodies or certain types of weapons, is addressed in various critical applications; in rescue missions after earthquakes or in military operations, etc. In such inverse problems, type of the wall is as important as the properties and location of the hidden target. Interpretation of the basic A-Scan GPR signals is a challenging task in the TTW target detection problem especially when the wall is constructed by bricks containing air-filled holes. In this paper, a simplified TTW target detection scenario is defined using cylindrical targets made of conductor or plastic materials. The target is placed behind the brick wall at different distances where the wall is made of either homogeneous solid bricks or inhomogeneous bricks that contain periodically located air-filled compartments. GPR signals are simulated for such target detection scenarios using a numerical computation tool that is based on the finite difference time domain (FDTD) technique. Then, simulated signals are analyzed in time domain for preprocessing and target detection. Energy based signal features are used to eliminate strong early-time reflections from the front face of the wall to enhance the signal components scattered by the target for better detection performance.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据