[1]龙卓,刘长军.一种双频基片集成波导介电常数测试系统[J].应用科技,2019,46(03):21-24.[doi:10.11991/yykj.201810008]
 LONG Zhuo,LIU Changjun.A dual-band substrate integrated waveguide permittivity measurement system[J].Applied science and technology,2019,46(03):21-24.[doi:10.11991/yykj.201810008]
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一种双频基片集成波导介电常数测试系统(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第46卷
期数:
2019年03期
页码:
21-24
栏目:
现代电子技术
出版日期:
2019-04-29

文章信息/Info

Title:
A dual-band substrate integrated waveguide permittivity measurement system
作者:
龙卓 刘长军
四川大学 电子信息学院, 四川 成都 610064
Author(s):
LONG Zhuo LIU Changjun
School of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
关键词:
基片集成波导双频传感器人工反演网络复介电常数微波测量谐振频率品质因数谐振腔
Keywords:
substrate integrated waveguidedual-band sensorartificial neural network (ANN)complex permittivitymicrowave measurementresonant frequencyquality factorresonant cavities
分类号:
TN972
DOI:
10.11991/yykj.201810008
文献标志码:
A
摘要:
为了同时测量不同频率下的复介电常数,设计了一种基于基片集成波导结构可工作于S和C波段的双频介电常数测量系统,在2.45和5.85 GHz附近可同时测量待测物的复介电常数。该测试系统的传感器包含2个按对角线级联的正方形谐振腔、2条测试缝隙以及一段微带馈电耦合结构。2条缝隙的工作波段相互独立,待测物接触传感器表面的2条缝隙影响系统的谐振频率和品质因数,基于人工神经网络的反演获得待测物复介电常数。仿真数据作为训练人工神经网络的样本,验证阶段,使用不同浓度的乙醇与水混合溶液检验传感器准确性,与理论值相比,在2.45 GHz时介电常数实部和虚部的测试结果最大相对误差为1.98%和1.28%,5.85 GHz时分别为2.15%和2.68%,该传感器具有较高的精度及双频测量特性。
Abstract:
In order to measure complex permittivity under different frequencies, a permittivity measurement system is designed to measure complex permittivities at S- and C-band, which is based on a substrate integrated waveguide structure. In the vicinity of 2.45 GHz and 5.85 GHz, the complex permittivities of the object can be measured simultaneously. The sensor of the test system includes two square resonant cavities cascaded diagonally, two test slots and a microstrip feed coupling structure. The working bands of the two slits are independent of each other, and the two slits on the surface of the object to be measured contacting the sensor affect the resonant frequency and quality factor of the system. The complex permittivity of the object to be measured was obtained based on the inversion of the artificial neural network. The simulation data were used to train the artificial neural network. In the verification stage, the accuracy of the sensor was checked by using mixed solutions of ethanol and water with different concentrations. Compared with the theoretical value, at 2.45 GHz, the maximum relative errors of the real and imaginary parts of complex permittivities were 1.98% and 1.28%; and at 5.85 GHz they were 2.51% and 2.68% respectively. The sensor has high precision and dual-frequency measurement characteristics.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-10-26。
基金项目:国家自然科学基金项目(61271074)
作者简介:龙卓,男,硕士研究生;刘长军,男,教授,博士生导师
通讯作者:刘长军,E-mail:cjliu@scu.edu.cn
更新日期/Last Update: 2019-04-29