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Performance Comparison of Automated Induction-Based Algorithms for Landmine Detection in a Blind Field Test

机译：盲场测试中基于自动感应的地雷检测算法性能比较

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摘要

Wideband Electromagnetic Induction (EMI) data provides an opportunity to apply robust statistical signal processing techniques to potentially mitigate false alarm rates in real-time landmine detection. This paper explores the application of matched subspace detectors (MSDs) and Support Vector Machines (SVMs) to this problem. A library of landmine responses is generated from a set of calibration data and a bank of matched subspace detectors, each designed to detect a specific mine type, is developed. Support vector machines are also considered for target/clutter discrimination. These are developed based on landmine signatures, decay rate estimates, and the outputs of matched subspace filter banks. Synthetic data sets are generated and matched subspace detectors and support vector machines are trained using this synthetic data. Receiver Operating Characteristics (ROCs) for matched sub-space detectors and support vector machines are presented for both experimental and simulated data sets. The results indicate that substantial reductions in false alarm rates can be achieved using these techniques, but that simulated data sets may not provide accurate predictors of performance.
机译：宽带电磁感应（EMI）数据为应用强大的统计信号处理技术提供了机会，以潜在地减轻实时地雷检测中的误报率。本文探讨了匹配子空间检测器（MSD）和支持向量机（SVM）在此问题上的应用。从一组校准数据中生成了一个地雷反应库，并开发了一组匹配的子空间探测器，每个探测器用于检测特定的地雷类型。支持向量机也被考虑用于目标/杂波识别。这些是根据地雷特征，衰减率估计以及匹配的子空间滤波器组的输出而开发的。生成综合数据集，并使用此综合数据训练匹配的子空间检测器和支持向量机。针对实验和模拟数据集，介绍了匹配子空间探测器和支持向量机的接收机工作特性（ROC）。结果表明，使用这些技术可以大大降低误报率，但是模拟数据集可能无法提供准确的性能预测指标。

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