A time-frequency feature prediction network for time-varying radio frequency interference
| Autor: | WAN Pengcheng, FENG Weike, TONG Ningning, WEI Wei |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Xibei Gongye Daxue Xuebao, Vol 41, Iss 3, Pp 587-594 (2023) |
| Druh dokumentu: | article |
| ISSN: | 1000-2758 2609-7125 |
| DOI: | 10.1051/jnwpu/20234130587 |
| Popis: | The time-varying radio frequency interference has strong nonlinear dynamic characteristics, which is difficult to be predicted by linear method effectively, making the anti-interference decision without sufficient information support. To solve this problem, a recurrent neural network for spectrum prediction based on time-frequency correlation features is proposed. A sliding window is used to characterize the two-dimensional correlation of time-frequency series, and the spectrum prediction problem is transformed into a problem similar to spatiotemporal sequence prediction. A gradient bridge structure across time frames is added to reduce the attenuation of the gradient in the long time and multi-level network propagation. The training efficiency and network performance are improved by the loss function with better matching. Simulation and experimental results verify the validity of the network prediction results. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|