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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Human Detection Based on Time-Varying Signature on Range-Doppler Diagram Using Deep Neural Networks
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Human Detection Based on Time-Varying Signature on Range-Doppler Diagram Using Deep Neural Networks

机译：利用深神经网络的范围 - 多普勒图对时变签名的人类检测

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摘要

We propose the detection of humans using millimeter-wave FMCW radar based on time-varying signatures of range-Doppler diagrams using deep recurrent neural networks (DRNNs). Demand for human detection is increasing for security, surveillance, and search and rescue purposes, recently, with a particular focus on urban areas filled with clutter and moving targets. We suggest the classification of targets based on their signatures in range-Doppler plots with time because the signatures can be consecutively observed. We measure five target types: humans, cars, cyclists, dogs, and road clutter using millimeter-wave FMCW radar that transmits fast chirps at 77 GHz. To maximize the classification accuracy using the time-varying range-Doppler signatures of the targets, we investigate and compare the performance of 2-D-deep convolutional neural networks (DCNN), 3-D-DCNN, and DRNN along with 2-D-DCNN. The DRNN plus 2-D-DCNN showed the best performance, and the classification accuracy yields 99%, with the human classification rate of 100%.
机译：我们提出了利用毫米波FMCW雷达的检测，基于使用深度反复性神经网络（DRNN）的范围多普勒图的时变签名。最近的安全，监督和搜救目的的对人类检测的需求正在增加，特别关注充满杂乱和移动目标的城市地区。我们建议基于它们的范围 - 多普勒图中的签名分类，因为可以连续观察到签名。我们衡量五种目标类型：使用毫米波FMCW雷达的人类，汽车，骑自行车者，狗和道路杂乱，可在77 GHz处传输快速啁啾。使用目标的时变范围多普勒签名来最大限度地提高分类准确性，我们调查并比较2-D-深卷积神经网络（DCNN），3-D-DCNN和DRNN以及2-D的性​​能的性能-dcnn。 DRNN加2-D-DCNN显示出最佳性能，分类精度产量为99％，人类分类率为100％。

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