Cargando…
Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference
In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple no...
Autores principales: | Wu, Xiaojun, Zhou, Yibo, Wu, Daolong, Xiao, Haitao, Lu, Yaya, Li, Hanbing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534624/ https://www.ncbi.nlm.nih.gov/pubmed/37765966 http://dx.doi.org/10.3390/s23187909 |
Ejemplares similares
-
Distributed Target Detection in Unknown Interference
por: Xu, Kaiming, et al.
Publicado: (2022) -
Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network
por: Zhang, Shengli, et al.
Publicado: (2021) -
DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion
por: Wang, Hongfeng, et al.
Publicado: (2022) -
Image Motion Deblurring Based on Deep Residual Shrinkage and Generative Adversarial Networks
por: Jiang, Wenbo, et al.
Publicado: (2022) -
Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification
por: Salimy, Alireza, et al.
Publicado: (2022)