Predictive Modeling of Maritime Radar Data Using Transformer Architecture

TL;DR

This paper reviews the use of transformer architectures for predictive modeling in maritime radar data, highlighting a significant research gap as no prior work has applied transformers to radar frame prediction despite their success in sonar and AIS data.

Contribution

It systematically analyzes existing transformer-based methods for maritime sensing and identifies the lack of radar frame prediction as a key research gap for future exploration.

Findings

Transformer architectures have been successful in AIS and sonar data prediction.

Maritime radar frame prediction with transformers has not been explored before.

The review highlights a critical gap and sets a direction for future research.

Abstract

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.