Convolution neural network and 77 GHz millimeter wave radar based intelligent liquid classification system

Journal of Information and Intelligence Pub Date : 2023-11-01 DOI:10.1016/j.jiixd.2023.06.001

Jiayu Chen, Xinhuai Wang, Yin Xu, Ye Peng, Wen Wang, Junyan Xiang, Qihang Xu

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Abstract

An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper. The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time. The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system. The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained. The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy. The result of the experiment is explained and the limitations of the experiment are discussed. It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification. The advantages of real time, non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.

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基于卷积神经网络和77 GHz毫米波雷达的智能液体分类系统

提出了一种基于77 GHz毫米波雷达和卷积神经网络的智能液体分类系统。数据由AWR1843雷达平台采集，由上位机的神经网络进行实时处理。首次尝试将雷达信号处理生成的多普勒热图作为系统的输入。分析了二维热图所携带的信息，并解释了多普勒热图可以用于分类的原因。可行的实验证明，该方法能够成功地对8种常见液体进行分类，并具有较高的准确率。对实验结果进行了说明，并讨论了实验的局限性。由此可见，FMCW毫米波雷达与卷积神经网络相结合是一种极具潜力的液体分类方法。实时、无创、单侧测量的优点也可用于危险液体的检测。

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