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.

翻译：海事自主系统需要具备稳健的预测能力，以预判船舶运动和环境动态。尽管Transformer架构已彻底改变了基于AIS的轨迹预测，并证明了其在声纳帧预测中的可行性，但其在海事雷达帧预测中的应用仍未被探索。鉴于雷达在导航中具备全天候可靠性，这一空白构成了一个关键缺口。本综述系统性地回顾了与海事雷达相关的预测建模方法，重点聚焦于用于时空序列预测的Transformer架构，并根据数据类型、架构和预测范围对现有代表性方法进行了分析。我们的综述表明，尽管现有文献已展示了基于Transformer的声纳传感帧预测，但尚无先前工作涉及基于Transformer的海事雷达帧预测，从而界定了一个明确的研究空白，并为该领域未来的工作指明了具体的研究方向。