AN/PSS-14 mine detection performance on beaches and in the surf zone

Steinway, William J.; Perry, Larry; Maningo, Richard; Ngan, Peter; Locke, Mark

doi:10.1117/12.542615

Cited by 1 publication

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“…As a consequence, GPR equipment have been recently been included in demining platforms as a complementary unit with a metal detector to achieve considerable improvements in detection performance, clearance rate, and a significant reduction in false alarms [25,26]. In terms of the technology readiness level, the most advanced systems are the US Army AN/PSS-14 (formerly Handheld Standoff Mine Detection System, HSTAMIDS) hand-held detector [27] developed by CyTerra (now L-3 Communications, US), the Anglo-German Vallon-Cobham VMR3 Minehound (formerly MINETECT) humanitarian detector [28], the ALIS (Advanced Land Mine Imaging System), developed by Tohoku University (Japan), and the CEIA (Italy) [29].…”

Section: Introductionmentioning

confidence: 99%

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

et al. 2020

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Among all the forensic applications in which it has become an important exploration tool, ground penetrating radar (GPR) methodology is being increasingly adopted for buried landmine localisation, a framework in which it is expected to improve the operations efficiency, given the high resolution imaging capability and the possibility of detecting both metallic and non-metallic landmines. In this context, this study presents landmine detection equipment based on multi-polarisation: a ground coupled GPR platform, which ensures suitable penetration/resolution performance without affecting the safety of surveys, thanks to the inclusion of a flexible ballistic shielding for supporting eventual blasts. The experimental results have shown that not only can the blanket absorb blast-induced flying fragments impacts, but that it also allows for the acquisition of data with the accuracy required to generate a correct 3D reconstruction of the subsurface. The produced GPR volume is then processed through an automated learning scheme based on a Convolutional Neural Network (CNN) capable of detecting buried objects with a high degree of accuracy.

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Section: Introductionmentioning

confidence: 99%