AI 中文总结
该研究针对建筑材料参数反演，提出基于FA-PSO算法，用自适应FA优化PSO超参数，优化高斯分布参数提高估计精度，推导CRLB作基准，数值结果表明方法对薄材料估计精度接近理论下界，能准确提取电磁特性支持无线性能评估。
AI 中文摘要
本文提出一种基于萤火虫粒子群优化（FA-PSO）算法的反演方法，利用自由空间法估计建筑材料的介电常数、电导率和厚度。为提高收敛效率和鲁棒性，采用自适应萤火虫算法（FA）系统优化粒子群优化（PSO）的超参数。通过优化用于种群初始化的高斯分布参数，逐步提高参数估计精度。此外，推导了复高斯噪声模型下介电常数、电导率和厚度的克拉美罗下界（CRLB），作为评估FA-PSO算法估计精度的理论基准。数值结果表明，对于较薄材料，该方法的估计精度接近理论下界，证实了反演框架的有效性。本研究准确提取了建筑材料的电磁特性，为评估其无线性能提供了有力支持。
英文摘要
In this paper, we propose an inversion method based on the firefly particle swarm optimization (FA-PSO) algorithm to estimate the permittivity, conductivity, and thickness of building materials using the free-space method. To improve convergence efficiency and robustness, an adaptive firefly algorithm (FA) is employed to systematically optimize the hyperparameters of the particle swarm optimization (PSO). By optimizing the parameters of the Gaussian distribution used for population initialization, the accuracy of parameter estimation is gradually improved. Furthermore, we derive the Cramer-Rao lower bound (CRLB) for the permittivity, conductivity, and thickness under a complex Gaussian noise model, which serves as a theoretical benchmark for evaluating the estimation accuracy of the FA-PSO algorithm. Numerical results indicate that for relatively thin materials, the estimation accuracy of the proposed method approaches this theoretical lower bound, confirming the effectiveness of the inversion framework. This study accurately extracts the electromagnetic properties of building materials, providing strong support for evaluating their wireless performance.
