用大模型提升海事雷达目标检测，先过滤无用信号再精准识别。
When marine radar target detection meets pretrained large language models
- 先分块筛选雷达信号，只保留有用片段，减少冗余。
- 将精选信号转为大模型能理解的嵌入，识别准确率显著提升。
- 只需微调归一化层，训练成本低，适合资源有限场景。
深度学习方法广泛用于从雷达回波信号序列中提取高维特征。然而，传统深度学习算法面临特征片段冗余、模型规模受限等问题。为此，我们提出一种融合特征预处理与预训练大语言模型（LLMs）的框架。预处理模块将雷达序列特征分块，通过补丁选择算法剔除无信息段，并将选中的补丁投影至与预训练大语言模型特征空间兼容的嵌入表示。利用这些优化后的嵌入，仅微调归一化层即可引入预训练大语言模型，显著降低训练负担的同时提升性能。在实测数据集上的实验表明，该方法在监督学习测试中显著优于现有最先进基线。
原文摘要 · Abstract (English)
Deep learning (DL) methods are widely used to extract high-dimensional patterns from the sequence features of radar echo signals. However, conventional DL algorithms face challenges such as redundant feature segments, and constraints from restricted model sizes. To address these issues, we propose a framework that integrates feature preprocessing with large language models (LLMs). Our preprocessing module tokenizes radar sequence features, applies a patch selection algorithm to filter out uninformative segments, and projects the selected patches into embeddings compatible with the feature space of pre-trained LLMs. Leveraging these refined embeddings, we incorporate a pre-trained LLM, fine-tuning only the normalization layers to reduce training burdens while enhancing performance. Experiments on measured datasets demonstrate that the proposed method significantly outperforms the state-of-the-art baselines on supervised learning tests.
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