RadarPLM: Adapting Pretrained Language Models for Marine Radar Target Detection with Preference-aware Loss

• URL: http://arxiv.org/abs/2509.12089v3
• Date: Mon, 03 Nov 2025 12:07:37 GMT
• Title: RadarPLM: Adapting Pretrained Language Models for Marine Radar Target Detection with Preference-aware Loss
• Authors: Qiying Hu,
• Abstract summary: We propose a novel fine-tuning framework for PLM-based marine radar target detection.<n>First, we design a lightweight adaptation module, enabling parameter-efficient fine-tuning.<n>Second, a novel preference-aware loss is developed to selectively optimize different feature patches.<n>Experiments on real-world marine radar datasets demonstrate that the proposed finetuning framework achieves an average performance improvement of 9.9%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in pre-trained language models (PLMs) have demonstrated their capabilities in capturing universal knowledge, making them promising applications for radar signal processing. 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. In this paper, we propose a novel fine-tuning framework for PLM-based marine radar target detection. First, we design a lightweight adaptation module, enabling parameter-efficient fine-tuning while preserving the pretrained model's general knowledge. Second, a novel preference-aware loss is developed to selectively optimize different feature patches based on their online evaluated learning values, guiding the model to concentrate on the most generalizable feature patterns during optimization. Extensive experiments on real-world marine radar datasets demonstrate that the proposed finetuning framework achieves an average performance improvement of 9.9% over the standard approach under low SCR conditions. Furthermore, the fine-tuned model, RadarPLM, consistently outperforms state-of-the-art detectors, particularly when training data are limited.

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