Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning
Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning
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DOI：
10.1109/radarconf2147009.2021.9455204
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
M. Mahbubur Rahman;S. Gurbuz
中科院分区：
文献类型：
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作者：
M. Mahbubur Rahman;S. Gurbuz
低成本射频传感器的广泛应用使得构建用于运动识别的射频传感器网络变得更加容易，同时也增加了各种频率、波形和传输参数下射频数据的可用性。然而，直接使用不同的射频传感器数据进行深度神经网络的训练是无效的，因为数据的现象学差异会导致显著的性能下降。在本文中，我们考虑了两种利用多频率射频数据的方法：1)单传感器情况，其中使用对抗域自适应将来自一个射频传感器的数据转换为类似于另一个射频传感器的数据；2)多传感器情况，其中设计了一个多模态神经网络，用于使用来自所有传感器的测量数据进行联合目标识别。我们的研究结果表明，开发的方法为利用多频射频传感器数据进行目标识别提供了有效的技术。
The widespread availability of low-cost RF sensors has made it easier to construct RF sensor networks for motion recognition, as well as increased the availability of RF data across a variety of frequencies, waveforms, and transmit parameters. However, it is not effective to directly use disparate RF sensor data for the training of deep neural networks, as the phenomenological differences in the data result in significant performance degradation. In this paper, we consider two approaches for the exploitation of multi-frequency RF data: 1) a single sensor case, where adversarial domain adaptation is used to transform the data from one RF sensor to resemble that of another, and 2) a multi-sensor case, where a multi-modal neural network is designed for joint target recognition using measurements from all sensors. Our results show that the developed approaches offer effective techniques for leveraging multi-frequency RF sensor data for target recognition.