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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.12508 |
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| _version_ | 1866909905549524992 |
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| author | Wang, Yanhao Wang, Lei Wang, Jie Liu, Yimin |
| author_facet | Wang, Yanhao Wang, Lei Wang, Jie Liu, Yimin |
| contents | Complex electromagnetic environments, often containing multiple jammers with different jamming patterns, produce non-uniform jamming power across the frequency spectrum. This spectral non-uniformity directly induces severe distortion in the target's HRRP, consequently compromising the performance and reliability of conventional HRRP-based target recognition methods. This paper proposes a novel, end-to-end trained network for robust radar target recognition. The core of our model is a CFA module that operates directly on the complex spectrum of the received echo. The CFA module learns to generate an adaptive frequency-domain filter, assigning lower weights to bands corrupted by strong jamming while preserving critical target information in cleaner bands. The filtered spectrum is then fed into a classifier backbone for recognition. Experimental results on simulated HRRP data with various jamming combinations demonstrate our method's superiority. Notably, under severe jamming conditions, our model achieves a recognition accuracy nearly 9% higher than traditional model-based approaches, all while introducing negligible computational overhead. This highlights its exceptional performance and robustness in challenging jamming environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12508 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Radar HRRP Recognition under Non-uniform Jamming Based on Complex-valued Frequency Attention Network Wang, Yanhao Wang, Lei Wang, Jie Liu, Yimin Signal Processing Complex electromagnetic environments, often containing multiple jammers with different jamming patterns, produce non-uniform jamming power across the frequency spectrum. This spectral non-uniformity directly induces severe distortion in the target's HRRP, consequently compromising the performance and reliability of conventional HRRP-based target recognition methods. This paper proposes a novel, end-to-end trained network for robust radar target recognition. The core of our model is a CFA module that operates directly on the complex spectrum of the received echo. The CFA module learns to generate an adaptive frequency-domain filter, assigning lower weights to bands corrupted by strong jamming while preserving critical target information in cleaner bands. The filtered spectrum is then fed into a classifier backbone for recognition. Experimental results on simulated HRRP data with various jamming combinations demonstrate our method's superiority. Notably, under severe jamming conditions, our model achieves a recognition accuracy nearly 9% higher than traditional model-based approaches, all while introducing negligible computational overhead. This highlights its exceptional performance and robustness in challenging jamming environments. |
| title | Robust Radar HRRP Recognition under Non-uniform Jamming Based on Complex-valued Frequency Attention Network |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.12508 |