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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.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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