Automatic Radar Signal Detection and FFT Estimation using Deep Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911786396024832 |
|---|---|
| author | Pemasiri, Akila Huang, Zi Williams, Fraser Goan, Ethan Denman, Simon Martin, Terrence Fookes, Clinton |
| author_facet | Pemasiri, Akila Huang, Zi Williams, Fraser Goan, Ethan Denman, Simon Martin, Terrence Fookes, Clinton |
| contents | This paper addresses a critical preliminary step in radar signal processing: detecting the presence of a radar signal and robustly estimating its bandwidth. Existing methods which are largely statistical feature-based approaches face challenges in electronic warfare (EW) settings where prior information about signals is lacking. While alternate deep learning based methods focus on more challenging environments, they primarily formulate this as a binary classification problem. In this research, we propose a novel methodology that not only detects the presence of a signal, but also localises it in the time domain and estimates its operating frequency band at that point in time. To achieve robust estimation, we introduce a compound loss function that leverages complementary information from both time-domain and frequency-domain representations. By integrating these approaches, we aim to improve the efficiency and accuracy of radar signal detection and parameter estimation, reducing both unnecessary resource consumption and human effort in downstream tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Radar Signal Detection and FFT Estimation using Deep Learning Pemasiri, Akila Huang, Zi Williams, Fraser Goan, Ethan Denman, Simon Martin, Terrence Fookes, Clinton Signal Processing This paper addresses a critical preliminary step in radar signal processing: detecting the presence of a radar signal and robustly estimating its bandwidth. Existing methods which are largely statistical feature-based approaches face challenges in electronic warfare (EW) settings where prior information about signals is lacking. While alternate deep learning based methods focus on more challenging environments, they primarily formulate this as a binary classification problem. In this research, we propose a novel methodology that not only detects the presence of a signal, but also localises it in the time domain and estimates its operating frequency band at that point in time. To achieve robust estimation, we introduce a compound loss function that leverages complementary information from both time-domain and frequency-domain representations. By integrating these approaches, we aim to improve the efficiency and accuracy of radar signal detection and parameter estimation, reducing both unnecessary resource consumption and human effort in downstream tasks. |
| title | Automatic Radar Signal Detection and FFT Estimation using Deep Learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2402.19073 |