Cargando…
An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars
With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but al...
Autores principales: | Xu, Xinsong, Bi, Daping, Pan, Jifei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269791/ https://www.ncbi.nlm.nih.gov/pubmed/35808475 http://dx.doi.org/10.3390/s22134980 |
Ejemplares similares
-
Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework
por: Pan, Jifei, et al.
Publicado: (2023) -
Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network
por: Zhang, Shengli, et al.
Publicado: (2021) -
An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors
por: Cuperman, Rafael, et al.
Publicado: (2022) -
A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition
por: MOHAMMED, Adam Ahmed Qaid, et al.
Publicado: (2019) -
A Radar Signal Recognition Approach via IIF-Net Deep Learning Models
por: Li, Ji, et al.
Publicado: (2020)