Improving Detection of a Portable NQR System for Humanitarian Demining Using Machine Learning
Improving Detection of a Portable NQR System for Humanitarian Demining Using Machine Learning
复制标题
DOI：
10.1109/tgrs.2021.3101226
复制
发表时间：
2021
影响因子：
8.2
通讯作者：
Yui Otagaki;J. Barras;Panagiotis Kosmas
中科院分区：
文献类型：
--
作者：
Yui Otagaki;J. Barras;Panagiotis Kosmas
本文提出了一种通过将机器学习 (ML) 应用于核四极共振 (NQR) 系统信号来增强埋地地雷检测的方法。这种定制的低成本便携式 NQR 系统专为人道主义排雷部署而开发，在这种情况下，强射频 (RF) 干扰和低信噪比 (SNR) 是准确检测的重要挑战。为了解决这些问题，我们在爆炸物研究部 X (RDX) 的实验室实验中对我们的系统获取的 NQR 信号应用并测试了各种 ML 技术。结果表明，ML 方法确实可以提高 NQR 设备的检测精度，并且使用我们设备的现场试验数据进一步证实了这一点。重要的是，经过训练的分类器可以使用我们设备的现场可编程门阵列 (FPGA) 架构来实现，并且与更简单但效率较低的基于快速傅里叶变换 (FFT) 的能量检测方法相比，运行时间损失很小。
This article presents an approach to enhance the detection of buried landmines by applying machine learning (ML) to signals from a nuclear quadrupole resonance (NQR) system. This custom-made, low-cost, and portable NQR system has been developed for deployment in humanitarian demining, where strong radio frequency (RF) interference and a low signal-to-noise ratio (SNR) are important challenges for accurate detection. To tackle these problems, we have applied and tested various ML techniques to NQR signals acquired by our system in laboratory experiments with the explosive Research Department X (RDX). Results suggest that ML methods can indeed improve the detection accuracy of the NQR device, and this is confirmed further using data from field trials with our device. Importantly, the trained classifiers can be implemented with our device's field-programmable gate array (FPGA) architecture and can run with little time penalty compared with simpler but less-efficient fast Fourier transform (FFT)-based energy detection methods.