【摘要】 能实时准确测量雪层厚度并进行预警的设备对于保护冬季长时间暴露在外界环境中的供电、通信和雷达等设备具有重要的应用价值。该文研究了基于微波矩形波导开口双腔体的雪层厚度测试方法，设计了对应的测量装置，给出了相关的构造、参数获取和数据反演策略。在此过程中，提出了基于单舱内嵌入金属隔板或频率选择表面(FSS)隔板的双腔双馈电双频段测试方法，通过大腔体低频大动态范围和小腔体高频高精度的策略结合参数相关处理算法，合理解决了大量程和高测试精度之间的矛盾。论文分析了自然降落覆盖在谐振腔开口处的不同雪层厚度对腔体的反射系数谐振频率和S参数的影响，并讨论了雪的密度、湿度对厚度测量精度的影响，比较了不同反演算法的效果，实现了1～30 mm的雪层厚度的分段测量，反演算法精度达到0.16 mm。测试精度优于1 mm。对应的技术和设备可直接或扩展用于以雪厚测试为代表的介质几何参数测试。更多还原

【Abstract】 Objective Large-scale winter snowfall poses a significant threat to the safety of outdoor infrastructure,including power transmission and communication systems. Real-time monitoring of snow depth within the range of 1～30 mm is required for accurate early warning and effective snow removal scheduling. Satellite-and radar-based techniques are mainly applied to snow depths exceeding 10 cm, but their large size and limited spatial resolution restrict their applicability to near-surface measurements. Although recently developed planar resonant sensors based on the resonance principle improve measurement accuracy, their effective measurement range remains limited. To resolve the trade-off between measurement range and accuracy, a rectangular microwave open resonant cavity featuring a dual-cavity, dual-feed, and dual-frequency-band configuration is proposed in combination with a data inversion algorithm. This scheme achieves a wide dynamic range of 1～30 mm while maintaining a measurement accuracy of 1 mm. The proposed device meets the monitoring requirements for snow depth corresponding to six snowfall intensity grades, ranging from light snow to heavy snowstorms.Methods The research methodology consists of four main stages. First, the phase-matching condition of the resonator formed by the open-ended waveguide and the snow layer is used to derive an analytical relationship between resonant frequency and snow depth, thereby verifying the feasibility of the measurement principle.Subsequently, a single-cavity model with coaxial feed is designed and simulated to evaluate its sensitivity to snow depths from 1 to 25 mm and to determine the corresponding operating frequency band. To further extend the measurement range, a dual-cavity, dual-feed model is constructed using either a metal plate or a Frequency Selective Surface(FSS) as a separator. A segmented measurement strategy is adopted, in which the large cavity and small cavity are responsible for different snow thickness intervals, enabling stable measurements with a precision of 1 mm over the full 1～30 mm range under different snow conditions. Finally, an optimal data inversion scheme is selected and implemented to further improve measurement accuracy.Results and Discussions A snow depth measurement technique based on a dual-band open-ended microwave resonant cavity is demonstrated. The dynamic measurement range is extended from 1～25 mm(Fig. 4) for the single-cavity configuration to 1～30 mm(Fig. 9) for the dual-cavity configuration. Simulation results show that the dual-cavity model maintains stable performance under variations in snow physical properties(Fig. 10～13).As snow depth increases, the resonant frequency exhibits a regular shift toward lower frequencies(Fig. 9(a)),whereas the attenuation remains below –10 dB(Fig. 9(b)), achieving a measurement precision of 1 mm.Experimental results show trends consistent with the simulations(Fig. 15). When combined with the data inversion scheme, the inversion error is less than 0.16 mm(Table 5), satisfying the requirements for both wide dynamic range and high measurement accuracy.Conclusions A dual-cavity, dual-feed, and dual-frequency snow depth measurement method employing either a metal plate or an FSS plate as a cavity separator is proposed. The limited dynamic range of conventional single-cavity designs is addressed through the constructed dual-cavity architecture. Measurement resolution is improved by assigning different snow thickness ranges to the two frequency bands and applying a data inversion algorithm. Experimental results demonstrate that the proposed method enables segmented measurement of snow depth from 1 to 30 mm, with an inversion accuracy of 0.16 mm and a measured precision better than 1 mm. The effects of variations in snow density and snow moisture content on resonant frequency and attenuation are analyzed. For future research, machine learning methods are suggested to associate measurement parameters with meteorological parameters, thereby improving measurement accuracy and extending the early-warning capability of the system.更多还原