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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.12089 |
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| _version_ | 1866915969074462720 |
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| author | Hu, Qiying Li, Yaowen Zhang, Shengyi Huang, Chuan Liu, Yu He, You |
| author_facet | Hu, Qiying Li, Yaowen Zhang, Shengyi Huang, Chuan Liu, Yu He, You |
| contents | Recent advances in pre-trained language models (PLMs) have demonstrated their capabilities in capturing universal knowledge, making them promising for radar signal processing applications. Nevertheless, directly fine-tuning PLMs on radar signals is both computationally expensive and prone to overfitting, particularly in low signal-to-clutter ratio (SCR) environments. To mitigate both issues, an effective fine-tuning framework for PLM-based marine radar target detection is proposed. First, we design a lightweight adaptation module, enabling computationally efficient fine-tuning while preserving the pre-trained model's general knowledge. Second, an effective selective fine-tuning strategy is developed to selectively optimize different feature patches based on their online-evaluated learning values, guiding the model to concentrate on those generalizable feature patterns and significantly reducing model overfitting to nosiy, anomalous, or overly simple patterns during optimization. Finally, a binary classification head is retrained based on autoencoder network to further enhance detection performance. Evaluations on real-world radar datasets highlight that the proposed RadarPLM framework considerably outperforms existing models, achieving a minimum of 6.35% gain in average detection performance under challenging low SCR conditions when using sequence features. In particular, under small-sample training conditions, RadarPLM also achieves highly significant average performance gains over prior methods, demonstrating the effectiveness of integrating the PLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12089 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning Hu, Qiying Li, Yaowen Zhang, Shengyi Huang, Chuan Liu, Yu He, You Signal Processing Computation and Language Recent advances in pre-trained language models (PLMs) have demonstrated their capabilities in capturing universal knowledge, making them promising for radar signal processing applications. Nevertheless, directly fine-tuning PLMs on radar signals is both computationally expensive and prone to overfitting, particularly in low signal-to-clutter ratio (SCR) environments. To mitigate both issues, an effective fine-tuning framework for PLM-based marine radar target detection is proposed. First, we design a lightweight adaptation module, enabling computationally efficient fine-tuning while preserving the pre-trained model's general knowledge. Second, an effective selective fine-tuning strategy is developed to selectively optimize different feature patches based on their online-evaluated learning values, guiding the model to concentrate on those generalizable feature patterns and significantly reducing model overfitting to nosiy, anomalous, or overly simple patterns during optimization. Finally, a binary classification head is retrained based on autoencoder network to further enhance detection performance. Evaluations on real-world radar datasets highlight that the proposed RadarPLM framework considerably outperforms existing models, achieving a minimum of 6.35% gain in average detection performance under challenging low SCR conditions when using sequence features. In particular, under small-sample training conditions, RadarPLM also achieves highly significant average performance gains over prior methods, demonstrating the effectiveness of integrating the PLM. |
| title | RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning |
| topic | Signal Processing Computation and Language |
| url | https://arxiv.org/abs/2509.12089 |