When marine radar target detection meets pretrained large language models

• URL: http://arxiv.org/abs/2509.12110v1
• Date: Mon, 15 Sep 2025 16:38:13 GMT
• Title: When marine radar target detection meets pretrained large language models
• Authors: Qiying Hu, Linping Zhang, Xueqian Wang, Gang Li, Yu Liu, Xiao-Ping Zhang,
• Abstract summary: We propose a framework that integrates feature preprocessing with large language models (LLMs)<n>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.<n> Experiments on measured datasets demonstrate that the proposed method significantly outperforms the state-of-the-art baselines on supervised learning tests.
• Score: 19.91452033424555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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