| Autor: |
Enze Guo, Hao Wu, Ming Guo, Yinan Wu, Jian Dong |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 129205-129216 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3332643 |
| Popis: |
Radar emitter classification plays an increasingly significant role in the electronic reconnaissance system. Due to many convolutional neural network (CNN)-based approaches suffer from insufficient spatial receptive fields and inadequate feature representation, the classification accuracy is poor in low signal-to-noise ratio (SNR) conditions. Therefore, in this paper, we stress the importance of multi-scale dilated convolutions for target feature extraction, and propose two novel CNN architecture design approaches called multi-scale dilated residual network (MDRN). By combining multi-scale dilated convolutions with residual architecture, MDRN not only has a larger receptive field, but also can learn more diverse features, thereby improving the ability to process time-frequency images (TFI) under high-noise energy conditions. Moreover, compared with the original residual model, MDRN does not increase any parameter complexity or floating-point operations per second (FLOPS). Experiments on the TFI classification task show that the proposed MDRN has superior performance over state-of-the-art CNN models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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