Simulator Driven Machine Learning on Embedded Systems for GPR Navigation and IED Detection, Akronym: MEDICI-LIBERTAD

哥伦比亚数十年的游击战争留下了数千枚地雷的致命遗产。2016年，间歇性地肆虐了50年的血腥内战终于结束，这对哥伦比亚来说可能是一个新的开始。波哥大政府和游击队组织哥伦比亚革命武装力量签署了人们期待已久的和平协议。此前，已有22万多人在国家安全部队、左翼叛军和右翼准军事组织的冲突中丧生。数百万人也被赶出了家园。哥伦比亚现在有机会改善其经济、政治文化和人权，或许还可以恢复其在国外的模糊形象。但对于哥伦比亚人民来说，日常生活仍然被危险所破坏。“Medici-Libertad”应用项目是“Medici”的两个后续项目之一，由DFG(MU 3507/3-1，RO 2493/4-1，SA 1035/6-1)和Colciencias在2014至2017年间资助。为了面对哥伦比亚排雷行动的巨大多样性，由于环境的变化和简易爆炸装置的建设，MEDICI-Libertad的主要目标是结合传感器融合方法研究硬件嵌入式人工神经元网络。这一方面包括用于探地雷达设备定位的传感器融合概念，另一方面包括模式识别支持的检测算法。关于机器学习所需的训练数据，必须提供大量排雷情景的大量和充足的数据。在这里，将研究自编写的2D-FDFD模拟器的适用性，该模拟器允许随机的GPR-IED模拟，包括与真实数据和人工数据的组合模拟。在合作伙伴看来，拟议的项目有可能改善探地雷达系统中神经网络支持的模式识别的硬件和软件水平。由于人道主义排雷行动是哥伦比亚和平进程中的一项重要任务，Halo-Trust等政府和非政府组织将通过评估实际地雷污染地区的调查程序来支持这一项目。此外，MEDICI项目的结果将直接纳入雷达讲座，该讲座由在哥伦比亚的德国合作伙伴每年举办，以保证哥伦比亚下一代工程师的可持续教育。

Decades of guerilla warfare in Colombia have left behind a deadly legacy of thousands of landmines. In what could be a new start for Colombia, in 2016 the bloody civil war which raged intermittently for five decades was finally brought to an end. A long hoped-for peace agreement was signed between the government in Bogotá and guerilla group FARC. Prior to this, over 220,000 people were killed in conflicts between state security forces, left-wing rebels, and right-wing paramilitaries. Millions of people were also driven from their homes. Colombia now has the chance to improve its economy, political culture, and human rights and perhaps also rehabilitate its murky image abroad. But for the people of Colombia, everyday life is still marred by danger. The country is still peppered with landmines from the armed conflict with the guerilleros.The applied project “MEDICI-LIBERTAD” is one of two follow-up projects of “MEDICI”, which was funded by DFG (MU 3507/3-1, RO 2493/4-1, SA 1035/6-1 ) and Colciencias between 2014 and 2017. In order to face the great diversity in Colombian demining operations, due to altering surroundings and IED construction, the main objective of “MEDICI-LIBERTAD” is to investigate hardware embedded artificial neuronal networks in combination with sensor fusion approaches. This includes sensor fusion concepts for the positioning of a GPR device on the one hand and pattern recognition supported detection algorithms on the other hand. Regarding the required training data for the machine learning, massive and adequate data of numerous demining scenarios must be available. Here, the suitability of a self-written 2D-FDFD simulator will be investigated, which allows for randomized GPR-IED simulations including combined simulations with real and artificial data. The essential parts of the aimed objectives are described in the following.In the view of the collaboration partners, the proposed project has potential to improve the state of the art regarding both, hardware and software for neuronal network supported pattern recognition in GPR systems. Here, an optimized GPR-IED simulator is applied to generate the required teaching data for the machine learning.Since humanitarian demining operations are an important task within the peace process in Colombia, governmental and non-governmental organizations like HALO-Trust will support this project by evaluating the investigated procedures in actual mine–contaminated areas. Moreover, findings of the MEDICI projects will be directly included in the RADAR lecture, which is hold every year by the German partners in Colombia in order to guarantee a sustainable education of Colombia’s next generation engineers.