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

TL;DR

This paper introduces a learning-based adaptive navigation filter for AUVs that dynamically tunes the process noise covariance matrix, improving accuracy and robustness over fixed or other adaptive methods.

Contribution

It proposes a novel hybrid adaptive filter that uses handcrafted features to learn and adjust the process noise covariance in real-time for INS/DVL fusion.

Findings

Simulation results demonstrate improved filter performance.

Adaptive tuning outperforms fixed covariance approaches.

Enhanced robustness in uncertain underwater environments.

Abstract

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.