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微带贴片天线的多项性能优化改进。

Multiple Performance Optimization for Microstrip Patch Antenna Improvement.

机构信息

Department of Communication Engineering, Feng Chia University, Taichung 40724, Taiwan.

Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan.

出版信息

Sensors (Basel). 2023 Apr 26;23(9):4278. doi: 10.3390/s23094278.

Abstract

As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

摘要

随着物联网(IoT)在我们日常生活中的应用越来越广泛,需要越来越多的无线通信设备,这意味着通过天线传输和接收的信号越来越多。因此,天线的性能在物联网应用中起着重要的作用,提高天线设计的效率已成为一个关键的课题。天线设计师通常使用电磁仿真工具来优化天线。虽然这种方法是可行的,但设计天线往往需要花费大量的时间。为了提高天线优化的效率,本文提出了一种用于天线优化的实验设计(DOE)方法。天线在每个方向上的长度和面积是实验参数,响应变量是天线增益和回波损耗。响应面法用于获得天线布局的最优参数。最后,我们利用天线仿真软件对天线优化的最优参数进行了验证,展示了 DOE 方法如何提高天线优化的效率。对 DOE 优化后的天线进行了实现,并对其测量结果进行了分析,结果表明,天线增益和回波损耗分别为 2.65 dBi 和 11.2 dB。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e70a/10181038/a22ad43fbb58/sensors-23-04278-g001.jpg

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