Regression analysis‐based identification model of key impactful parameters on buried antennas

Abstract

Summary The antenna's bandwidth is strongly dependent on its physical properties and the environment where it is located. Nevertheless, for transmitting devices embedded permanently in the soil, the propagation medium's properties are unstable due to dielectric permittivity's spatial and temporal changes. Relying on soil parameters values, we present three new multivariable regression models allowing a good fit to the dataset curves with antenna's resonant frequency variations prediction of about 98%. Precisely, such models have been obtained with the help of the multiple linear regression analysis method. Additionally, the results revealed that amongst four considered parameters (i.e., volumetric moisture, bulk density, specific density, and temperature), volumetric moisture is the most influencing factor on the antenna operating frequency band with a correlation coefficient of −0.989, whereas it equals 0.04, −0.035, and −0.002 for the temperature, bulk density, and specific density, respectively.

A microstrip Antenna for Underground Applications with Frequency Insenitivity to soil moisture variations

“…However, it's noteworthy that in such scenarios, the antenna may face challenges stemming from the dynamic nature of certain soil properties. Specifically, the dielectric permittivity is dependent on several intrinsic parameters such as bulk and specific density, and also extrinsic parameters like temperature and volumetrc water content (VWC) causing disturbances on the resonance frequency [13], bandwidth [14], and directivity [15]. This characteristic can indeed be exploited for the sensing functions to capture variations of the parameters mentioned above [16][17][18].…”