Abstract
Liquid identification is an essential technology for water safety monitoring. This paper shows the feasibility of identifying liquid using millimeter wave (mmWave) signals. The inherent principle comes from that the fine-grained mmWave signals can capture signal attenuation, phase shift, and propagation delay when penetrating the liquid. We have conducted a preliminary experiment to prove the effectiveness of using mmWave for liquid identification. However, after moving the container, the identification accuracy will drop significantly. To address this challenge, we propose a robust mmWave-based liquid identification approach MmLiquid, which uses a container position information filtering (CPIF) scheme to eliminate the influence of different container positions. MmLiquid will extract container position-independent information from the original mmWave signals and train a deep complex model (DCN) for accurate liquid identification. To further improve the identification performance, we set up an identification environment with two reflective surfaces to capture effective mmWave signals that contain more liquids information. We implement MmLiquid using commercial mmWave devices. Experimental results on 16 kinds of liquids at 24 different container positions show that MmLiquid can achieve an average liquid identification accuracy of 97.6%.
This work is supported by NSFC under grant no. 62072396, Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars under grant no. LR19F020001, the Fundamental Research Funds for the Central Universities (no. 226-2022-00087), and Alibaba-Zhejiang University Joint Institute of Frontier Technologies.
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Cao, D., Lin, Y., Ren, G., Gao, Y., Dong, W. (2022). MmLiquid: Liquid Identification Using mmWave. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_1
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