LSTM Framework for Classification of Radar and Communications Signals
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929666378432512 |
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| author | Clerico, Victoria Gonzalez-Lopez, Jorge Agam, Gady Grajal, Jesus |
| author_facet | Clerico, Victoria Gonzalez-Lopez, Jorge Agam, Gady Grajal, Jesus |
| contents | Although radar and communications signal classification are usually treated separately, they share similar characteristics, and methods applied in one domain can be potentially applied in the other. We propose a simple and unified scheme for the classification of radar and communications signals using Long Short-Term Memory (LSTM) neural networks. This proposal provides an improvement of the state of the art on radar signals where LSTM models are starting to be applied within schemes of higher complexity. To date, there is no standard public dataset for radar signals. Therefore, we propose DeepRadar2022, a radar dataset used in our systematic evaluations that is available publicly and will facilitate a standard comparison between methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03192 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LSTM Framework for Classification of Radar and Communications Signals Clerico, Victoria Gonzalez-Lopez, Jorge Agam, Gady Grajal, Jesus Signal Processing Although radar and communications signal classification are usually treated separately, they share similar characteristics, and methods applied in one domain can be potentially applied in the other. We propose a simple and unified scheme for the classification of radar and communications signals using Long Short-Term Memory (LSTM) neural networks. This proposal provides an improvement of the state of the art on radar signals where LSTM models are starting to be applied within schemes of higher complexity. To date, there is no standard public dataset for radar signals. Therefore, we propose DeepRadar2022, a radar dataset used in our systematic evaluations that is available publicly and will facilitate a standard comparison between methods. |
| title | LSTM Framework for Classification of Radar and Communications Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2305.03192 |