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.

提出一种基于学习的自适应速度辅助导航滤波器，通过生成特征值以调整瞬时系统噪声协方差矩阵，以提高滤波器性能。