Robust Convergence Technique Against Multilevel Random Effects in Stochastic Modeling of Wearable Antennas' Far Field

Stochastic modeling is widely employed to characterize uncertainty propagation in fluctuating wearable antenna systems. A major challenge that hinders the convergence of stochastic models is the multilevel random effects on antenna's far field caused by random disturbances, which exacerbate the already difficult inherent issue tied to high dimensionality and nonlinearity. This letter proposes to separately model the 'global' random effect depending mainly on frequency and the 'fine' random effect depending mainly on antenna's directional characteristics. The 'decoupling' of global and fine effects is obtained by separately modeling the reflection coefficient S11 and a newly defined 'desensitized' far field, which is insensitive to detuning (or mismatch) phenomena. A 'centering' technique based on cross correlation is used to reduce the sensibility of S11 to the randomness. The whole strategy significantly accelerates the convergence of the modeling process, resulting in a 'bi-level' surrogate model that exhibits enhanced robustness and accuracy. Comparative tests on a flexible textile patch antenna demonstrate that the proposed technique can reduce modeling costs by 57% while maintaining the same level of model accuracy. The proposed solution could expand the application of stochastic modeling to a broader spectrum of antenna characterization and optimization.