用Transformer预测海事雷达数据，填补了全天候导航的关键空白
Predictive Modeling of Maritime Radar Data Using Transformer Architecture
- 采用Transformer架构处理雷达时空序列数据
- 现有研究未涉及雷达帧预测，存在明显空白
- 适合从事海事自主系统与雷达建模的研究者
海上自主系统需要强大的预测能力来预判船舶运动与环境动态。尽管Transformer架构已在AIS轨迹预测中取得突破，并展现出声呐帧预测的可行性，但其在海事雷达帧预测中的应用仍属空白，而雷达具有全天候导航可靠性，这一缺口尤为关键。本文系统回顾了与海事雷达预测相关的建模方法，重点分析基于Transformer的时空序列预测架构，从数据类型、模型结构和预测时长等维度评估现有代表性方法。研究表明，虽然已有工作实现了基于Transformer的声呐帧预测，但尚未有研究探索基于Transformer的海事雷达帧预测，明确了这一研究空白，并为未来该领域的工作指明了具体方向。
原文摘要 · Abstract (English)
Maritime autonomous systems require robust predictive capabilities to anticipate vessel motion and environmental dynamics. While transformer architectures have revolutionized AIS-based trajectory prediction and demonstrated feasibility for sonar frame forecasting, their application to maritime radar frame prediction remains unexplored, creating a critical gap given radar's all-weather reliability for navigation. This survey systematically reviews predictive modeling approaches relevant to maritime radar, with emphasis on transformer architectures for spatiotemporal sequence forecasting, where existing representative methods are analyzed according to data type, architecture, and prediction horizon. Our review shows that, while the literature has demonstrated transformer-based frame prediction for sonar sensing, no prior work addresses transformer-based maritime radar frame prediction, thereby defining a clear research gap and motivating a concrete research direction for future work in this area.
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