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Background Adaptive Division Filtering for Hand-Held Ground Penetrating Radar

机译:手持式探地雷达的背景自适应除法滤波

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The challenge in detecting explosive hazards is that there are multiple types of targets buried at different depths in a highly-cluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Such explosive hazards are typically deployed in past and present war zones and they pose a grave threat to the safety of civilians and soldiers alike. This paper focuses on a new image enhancement technique for hand-held ground penetrating radar (GPR). Advantages of the proposed technique is it runs in real-time and it does not require the radar to remain at a constant distance from the ground. Herein, we evaluate the performance of the proposed technique using data collected from a U.S. Army test site, which includes targets with varying amounts of metal content, placement depths, clutter and times of day. Receiver operating characteristic (ROC) curve-based results are presented for the detection of shallow, medium and deeply buried targets. Preliminary results are very encouraging and they demonstrate the usefulness of the proposed filtering technique.
机译:检测爆炸危险的挑战在于,在高度混乱的环境中,有多种类型的目标埋在不同的深度。存在各种各样的目标和混乱签名,这使得检测算法设计变得困难。这种爆炸性危险通常部署在过去和现在的战区,对平民和士兵的安全构成严重威胁。本文着重介绍一种用于手持式探地雷达(GPR)的新图像增强技术。所提出的技术的优点是它可以实时运行,并且不需要雷达与地面保持恒定的距离。在本文中,我们使用从美国陆军测试地点收集的数据评估提出的技术的性能,该数据包括金属含量,放置深度,混乱情况和一天中不同时间的目标。提出了基于接收器工作特性(ROC)曲线的结果,用于检测浅,中和深埋目标。初步结果令人鼓舞,它们证明了所提出的过滤技术的有用性。

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