# A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion

arXiv: https://arxiv.org/abs/2211.01329 · HTML: https://mlsanity.com/paper/2211.01329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 8 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation](https://mlsanity.com/paper/2206.13603/) (2022)
- [A Hybrid Model and Learning-Based Adaptive Navigation Filter](https://mlsanity.com/paper/2207.12082/) (2022)
- [Learning Vehicle Trajectory Uncertainty](https://mlsanity.com/paper/2206.04409/) (2022)
- [Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation](https://mlsanity.com/paper/2210.02930/) (2022)

## Built on by (most cited first, 2 of 2)

- [Data-Driven Strategies for Coping with Incomplete DVL Measurements](https://mlsanity.com/paper/2401.15620/) (2024)
- [Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors](https://mlsanity.com/paper/2607.05663/) (2026)

## Abstract (arXiv)

Autonomous underwater vehicles (AUV) are commonly used in many underwater applications. Usually, inertial sensors and Doppler velocity log readings are used in a nonlinear filter to estimate the AUV navigation solution. The process noise covariance matrix is tuned according to the inertial sensors' characteristics. This matrix greatly influences filter accuracy, robustness, and performance. A common practice is to assume that this matrix is fixed during the AUV operation. However, it varies over time as the amount of uncertainty is unknown. Therefore, adaptive tuning of this matrix can lead to a significant improvement in the filter performance. In this work, we propose a learning-based adaptive velocity-aided navigation filter. To that end, handcrafted features are generated and used to tune the momentary system noise covariance matrix. Once the process noise covariance is learned, it is fed into the model-based navigation filter. Simulation results show the benefits of our approach compared to other adaptive approaches.