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Software Defined Doppler Radar for Landmine Detection using GA-Optimized Machine Learning

机译：使用GA优化的机器学习进行地雷探测的软件定义多普勒雷达

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摘要

Using software defined radar (SDR) technology and modern machine learning algorithms, this paper demonstrates a software defined Doppler Radar(SDDRadar) system that can distinguish buried non-metallic landmine from other buried objects (rock, wood, etc.) with a high accuracy. In the sensing, the spectrum responses of different buried objects are collected using the SDDRadar. The vibration spectrum data are fed into a Random Forest where a Genetic Algorithm(GA) is designed to optimize the performance of the Random Forest. To leverage SDDRadar sensitivity, a clutter cancellation circuit is designed and integrated into the system. Two outdoor tests are performed under dry and wet soil conditions. For performance evaluation, the GA-optimized Random Forest is compared with other two machine learning algorithms, including Support Vector Machine and Logistic Regression. As it turns out, the GA-optimized Random Forest has the best classification performance in terms of both precision and recall parameters.
机译：本文使用软件定义的雷达（SDR）技术和现代机器学习算法，演示了一种软件定义的多普勒雷达（SDDRadar）系统，该系统可以高精度地区分掩埋的非金属地雷与其他掩埋的物体（岩石，木材等） 。在感测中，使用SDDRadar收集了不同掩埋物体的光谱响应。振动谱数据被输入到随机森林中，在该森林中设计了遗传算法（GA）以优化随机森林的性能。为了利用SDDRadar的灵敏度，设计了杂波消除电路并将其集成到系统中。在干燥和潮湿的土壤条件下进行了两个室外测试。为了进行性能评估，将GA优化的随机森林与其他两种机器学习算法（包括支持向量机和Logistic回归）进行了比较。事实证明，就精度和召回参数而言，经过GA优化的随机森林均具有最佳的分类性能。

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