Predictive Modelling of Maritime Radar Data Using Transformer Architecture

Predicting vessel motion and environmental dynamics is essential for safe operation of autonomous maritime navigation systems. Transformer-based models have achieved strong results in AIS-trajectory forecasting and in anticipating future sonar observations, however, their use in maritime radar frame prediction has received little attention, despite radar being a key sensing modality in challenging weather and visibility conditions. In an effort to address this gap, this paper introduces a transformer architecture for predicting future maritime radar frames from sequences of past X-band observations and vessel ego-motion derived from GNSS, adapting the EchoPT paradigm originally developed for simulated in-air sonar imagery to the real-world MOANA dataset. We detail the model architecture and evaluate its prediction performance under both single-frame and autoregressive settings on held-out test data, and benchmark the model against persistence and rigid geometric warp references. A complementary failure mode analysis links the observed prediction errors to specific architectural and dataset choices, providing concrete directions for further research.