# Predictive Modeling of Maritime Radar Data Using Transformer Architecture

arXiv: https://arxiv.org/abs/2512.17098 · HTML: https://mlsanity.com/paper/2512.17098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021)
- [MaskViT: Masked Visual Pre-Training for Video Prediction](https://mlsanity.com/paper/2206.11894/) (2022)
- [Deep Learning Meets SAR](https://mlsanity.com/paper/2006.10027/) (2020)
- [TrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction](https://mlsanity.com/paper/2109.03958/) (2021)
- [VPTR: Efficient Transformers for Video Prediction](https://mlsanity.com/paper/2203.15836/) (2022)
- [Video Prediction by Efficient Transformers](https://mlsanity.com/paper/2212.06026/) (2022)
- [Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment](https://mlsanity.com/paper/2210.08487/) (2022)
- [AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Review](https://mlsanity.com/paper/2505.07374/) (2025)
- [Adaptive Acoustic Flow-Based Navigation with 3D Sonar Sensor Fusion](https://mlsanity.com/paper/2208.10823/) (2022)
- [MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application](https://mlsanity.com/paper/2412.03887/) (2024)
- [EchoPT: A Pretrained Transformer Architecture that Predicts 2D In-Air Sonar Images for Mobile Robotics](https://mlsanity.com/paper/2405.12573/) (2024)
- [Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance](https://mlsanity.com/paper/2406.07399/) (2024)

## Abstract (arXiv)

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.