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首页> 外文会议>Detection and sensing of mines, expolosive objects, and obscured targets XIX >Advanced EMI Models for Survey Data Processing: Targets Detection and Classification
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Advanced EMI Models for Survey Data Processing: Targets Detection and Classification

机译：用于调查数据处理的高级EMI模型：目标检测和分类

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摘要

One of the most challenging aspects of survey data processing is target selection. The fundamental input for the classification is dynamic data collected along survey lines. These data are different from the static data obtained in cued mode and used for target classification. Survey data are typically collected using just one transmitter loop (the Z-axis loop) and feature short data point collection times and short decay transience. The collection intervals for each data point are typically 0.1 s, and the signal repetition rates are typically 90 or 270 Hz (in other words, the transient decay times are 2.7 ms or 0.9 ms). Reliable classification requires multiple side/angle illumination; i.e., to conduct reliable classification it is necessary to combine and jointly invert multiple data points. However, picking data points that provide optimal information for classifying targets is a difficult task. The traditional method plots signal amplitudes on a 2D map and picks peaks of signal level without properly accounting for the underlying physics. In this paper, the joint diagonalization is applied to survey data sets to improve data pre-processing and target picking. The JD technique is an EMI data analysis and target classification technique and is applicable for all next-generation multi-static array EMI sensors. The method extracts multi-static response data matrix eigenvalues. The eigenvalues are main characteristics of the data. Recent studies have demonstrated that the method has great potential to quickly estimate the number of potential targets and moreover classify these targets at the data pre-processing stage, in real time and without the need for a forward model. Another advantage of JD is that it provides the ability to separate signal from noise making it possible to de-noise data without distorting the signal due to the targets. In this paper the JD technique is used to process dynamic data collected at South West Proving Ground and Aberdeen Proving Ground (APG) sites using the 2 × 2 TEMTADS and OPTEMA systems, respectively. The joint eigenvalues are extracted as functions of time for each data point and summed/stacked together before being used to create detection maps. Once targets are detected, a set of data is chosen for each anomaly and inverted using the ortho-normalized volume magnetic source technique.
机译：调查数据处理最具挑战性的方面之一是目标的选择。分类的基本输入是沿调查线收集的动态数据。这些数据不同于以提示模式获得的静态数据，用于目标分类。通常仅使用一个发射器回路（Z轴回路）收集调查数据，并且具有较短的数据点收集时间和较短的衰减瞬变。每个数据点的收集间隔通常为0.1 s，信号重复率通常为90或270 Hz（换句话说，瞬态衰减时间为2.7 ms或0.9 ms）。可靠的分类需要多个侧面/角度照明；即，为了进行可靠的分类，有必要组合并共同反转多个数据点。但是，选择提供最佳信息以对目标进行分类的数据点是一项艰巨的任务。传统方法在2D地图上绘制信号幅度并选择信号电平的峰值，而没有适当考虑基础物理原理。在本文中，联合对角线化应用于调查数据集以改善数据预处理和目标挑选。 JD技术是EMI数据分析和目标分类技术，适用于所有下一代多静态阵列EMI传感器。该方法提取多静态响应数据矩阵特征值。特征值是数据的主要特征。最近的研究表明，该方法具有巨大潜力，可以快速估计潜在目标的数量，并且可以在数据预处理阶段实时，实时地对这些目标进行分类，而无需使用正向模型。 JD的另一个优点是它提供了将信号与噪声分离的能力，从而可以对数据进行降噪而不会由于目标而使信号失真。本文采用JD技术分别使用2×2 TEMTADS和OPTEMA系统处理在西南试验场和阿伯丁试验场（APG）站点收集的动态数据。对于每个数据点，提取联合特征值作为时间的函数，并将其相加/叠加在一起，然后再用于创建检测图。一旦检测到目标，就为每个异常选择一组数据，并使用正交归一化体积磁源技术对其进行反转。

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