在时变的无线场景中传输的高空核爆电磁脉冲（high altitude electromagnetic pulse, HEMP）不可避免地产生波形畸变，导致HEMP参数识别精确度显著地降低。为解决这一难题，考虑无线信道对HEMP波形的影响，利用其波形特征，研究基于极限学习机参数识别网络（extreme learning machine parameter identification network, ELM-PInet）的参数识别方法，以改善HEMP参数识别精确度。首先，从无线传输理论出发，构建HEMP波形传输模型，诠释无线信道对HEMP波形的非线性影响。随后，构建ELM-PInet进行波形畸变抑制，改善HEMP参数识别精度。最后，基于实验平台，对提出方法进行了现场辐照实验验证。仿真结果表明，相比于经典的HEMP参数识别方法，提出方法可改善HEMP参数的识别精度；针对不同的参数影响，ELM-PInet参数识别方法具有鲁棒性。同时，通过现场辐照实验进一步验证了提出方法的有效性。

In dynamic wireless environments, the distortion of transmission waveform is inevitably present, deteriorating the accuracy of identifying high altitude electromagnetic pulse (HEMP) parameters. To address this issue, an extreme learning machine parameter identification network (ELM-PInet)-based parameter identification method was investigated, which leverages the characteristics of HEMP waveform and considers the impact of wireless channels, thereby improving the accuracy of HEMP parameter identification. To demonstrate the nonlinear effects of wireless channels, the transmission model of HEMP waveform was first constructed based on wireless transmission theory. Subsequently, an ELM-PInet was developed to suppress waveform distortion and improve the identification accuracy of HEMP parameters. Finally, the proposed method was validated through field irradiation test on the experimental platform. Simulation results demonstrate that compared to classical HEMP parameter identification methods, the identification accuracy of HEMP parameters is enhanced by the proposed method. Furthermore, the ELM-PInet-based parameter identification method exhibits its robustness against the impacts of different parameters. Additionally, the effectiveness of the proposed method is further validated through field irradiation experiments.