ProNet: Adaptive Process Noise Estimation for INS/DVL Fusion

TL;DR

ProNet introduces a hybrid adaptive method for estimating process noise in INS/DVL fusion, improving navigation accuracy for underwater vehicles by using inertial sensor data to dynamically tune filter parameters.

Contribution

It presents a novel hybrid approach that adaptively estimates process noise covariance using inertial data, enhancing filter robustness during AUV missions.

Findings

ProNet outperforms existing models in simulation tests.

Adaptive noise estimation improves navigation accuracy.

The method requires only inertial sensor data.

Abstract

Inertial and Doppler velocity log sensors are commonly used to provide the navigation solution for autonomous underwater vehicles (AUV). To this end, a nonlinear filter is adopted for the fusion task. The filter's process noise covariance matrix is critical for filter accuracy and robustness. While this matrix varies over time during the AUV mission, the filter assumes a constant matrix. Several models and learning approaches in the literature suggest tuning the process noise covariance during operation. In this work, we propose ProNet, a hybrid, adaptive process, noise estimation approach for a velocity-aided navigation filter. ProNet requires only the inertial sensor reading to regress the process noise covariance. Once learned, it is fed into the model-based navigation filter, resulting in a hybrid filter. Simulation results show the benefits of our approach compared to other models and learning adaptive approaches.