# Neural-Assisted in-Motion Self-Heading Alignment

arXiv: https://arxiv.org/abs/2604.00168 · HTML: https://mlsanity.com/paper/2604.00168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 31 Mar 2026.

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

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- [A survey on modern trainable activation functions](https://mlsanity.com/paper/2005.00817/) (2020)
- [Overfitting Mechanism and Avoidance in Deep Neural Networks](https://mlsanity.com/paper/1901.06566/) (2019)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Adaptive Kalman-Informed Transformer](https://mlsanity.com/paper/2401.09987/) (2024)
- [Rapid Gyroscope Calibration: A Deep Learning Approach](https://mlsanity.com/paper/2409.00488/) (2024)
- [DCNet: A Data-Driven Framework for DVL Calibration](https://mlsanity.com/paper/2410.08809/) (2024)
- [Towards Learning-Based Gyrocompassing](https://mlsanity.com/paper/2312.12121/) (2023)
- [WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning](https://mlsanity.com/paper/2503.13568/) (2025)
- [Transformer-Based Robust Underwater Inertial Navigation in Prolonged Doppler Velocity Log Outages](https://mlsanity.com/paper/2504.07697/) (2025)

## Abstract (arXiv)

Autonomous platforms operating in the oceans require accurate navigation to successfully complete their mission. In this regard, the initial heading estimation accuracy and the time required to achieve it play a critical role. The initial heading is traditionally estimated by model-based approaches employing orientation decomposition. However, methods such as the dual vector decomposition and optimized attitude decomposition achieve satisfactory heading accuracy only after long alignment times. To allow rapid and accurate initial heading estimation, we propose an end-to-end, model-free, neural-assisted framework using the same inputs as the model-based approaches. Our proposed approach was trained and evaluated on real-world dataset captured by an autonomous surface vehicle. Our approach shows a significant accuracy improvement over the model-based approaches achieving an average absolute error improvement of 53%. Additionally, our proposed approach was able to reduce the alignment time by up to 67%. Thus, by employing our proposed approach, the reduction in alignment time and improved accuracy allow for a shorter deployment time of an autonomous platform and increased navigation accuracy during the mission.