When marine radar target detection meets pretrained large language models
This work addresses marine radar target detection, an incremental advancement for domain-specific applications.
The paper tackles the problem of redundant features and model size constraints in marine radar target detection by integrating feature preprocessing with pretrained large language models, achieving significant performance improvements over state-of-the-art baselines in supervised learning tests.
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.