RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning

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. 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 computationally efficient fine-tuning while preserving the pre-trained 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 those generalizable feature patterns during optimization. Finally, a binary classification head is retrained based on autoencoder network to further enhance detection performance. Experiments on real-world radar data show that the proposed RadarPLM framework yields at least a 6.35% improvement in detection performance over the existing networks under low SCR conditions. Especially, in small training samples cases,the proposed RadarPLM also achieves significant advantage over existing networks owing to the incorporation of the PLM.