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Localization and Activity Classification of Unmanned Aerial Vehicle Using mmWave FMCW Radars

机译：使用MMWAVE FMCW雷达的无人空中车辆的本地化和活动分类

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摘要

In this article, we present a novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar. The localization and activity classification for aerial vehicle enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications. In the proposed method, Radar's antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. The height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival. The aerial vehicle's activity is classified using machine learning methods on micro-Doppler signatures extracted from Radar measurements taken in an outdoor environment. To evaluate performance, various light weight classification models such as logistic regression, support vector machine (SVM), Light gradient boosting machine (GBM), and a custom lightweight convolutional neural network (CNN) are investigated. Based on the results, the logistic regression, SVM, and Light GBM achieve an accuracy of 93%. Furthermore, the custom lightweight CNN can achieve activity classification accuracy of 95%. The performance of the proposed lightweight CNN is also compared with the pre-trained models (VGG16, VGG19, ResNet50, ResNet101, and InceptionResNet). The proposed lightweight CNN suits best for embedded and/or edge computing devices.
机译：在本文中，我们为使用MMWAVE频率调制的连续波（FMCW）雷达提供了一种新的本地化和活动分类方法，用于空中车辆。空中车辆的本地化和活动分类能够利用安全监控和隐私监测应用中的MM波雷达。在所提出的方法中，雷达的天线垂直定向以测量飞行器从地面站到达的仰角。使用测量的径向范围和到达角度，估计空中车辆与雷达站的空中车辆的水平距离。空中车辆的活动是在室外环境中提取的雷达测量中提取的微多普勒签名上的机器学习方法进行分类。为了评估性能，研究了各种轻重分类模型，如逻辑回归，支持向量机（SVM），光梯度升压机（GBM）和定制轻型卷积神经网络（CNN）。基于结果，Logistic回归，SVM和Light GBM实现了93％的准确性。此外，定制轻量级CNN可以实现95％的活动分类精度。建议的轻量级CNN的性能也与预先训练的型号（VGG16，VGG19，RESET50，RESET101和InceptionResNet）进行比较。所提出的轻量级CNN适用于嵌入式和/或边缘计算设备。

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