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

• URL: http://arxiv.org/abs/2509.12089v1
• Date: Mon, 15 Sep 2025 16:16:57 GMT
• Title: RadarLLM: Adapting Pretrained Large Language Models for Marine Radar Target Detection with Preference-aware Loss
• Authors: Qiying Hu,
• Abstract summary: We introduce RadarLLM, a novel fine-tuning framework that utilizes an effective preference-aware loss.<n>RadarLLM consistently outperforms state-of-the-art baselines across diverse detection scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in pre-trained large language models (LLMs) have demonstrated their capacities to capture universal knowledge, making them promising general-purpose optimization solvers for wireless signal processing. Motivated by these findings, we take the first step towards fine-tuning pre-trained LLMs for the effective analysis of radar signal features in marine target detection tasks. Nevertheless, directly fine-tuning pre-trained LLMs on marine target detection tasks tends to suffer from pronounced overfitting, particularly in challenging low signal-to-clutter ratio (SCR) scenarios. This overfitting primarily stems from the model's tendency to memorize spurious or noisy feature patterns rather than learning discriminative structures that generalize well to unseen data. To address this challenge, we introduce RadarLLM, a novel fine-tuning framework that utilizes an effective preference-aware loss. Unlike conventional training strategies that uniformly optimize all feature tokens, this loss function selectively optimizes different feature patches based on their online evaluated learning values, thus guiding the model to focus on the most generalizable patterns during optimization. We theoretically demonstrate the effectiveness of the evaluated learning values by transforming the problem as selecting useful feature tokens. Extensive experiments on real-world marine radar datasets show that 1) the proposed loss function is much better than the original one, with particularly significant gains in challenging low SCR scenarios and 2) RadarLLM consistently outperforms state-of-the-art baselines across diverse detection scenarios, with particularly notable gains under limited training data conditions.

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