A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion
This work addresses navigation accuracy for autonomous underwater vehicles, but it is incremental as it builds on existing adaptive tuning methods.
The paper tackled the problem of improving navigation filter performance for autonomous underwater vehicles by adaptively tuning the process noise covariance matrix, which varies over time, using a learning-based approach; simulation results demonstrated benefits compared to other adaptive methods.
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.