The extended Kalman filter (EKF) is a widely adopted method for sensor fusion
in navigation applications. A crucial aspect of the EKF is the online
determination of the process noise covariance matrix reflecting the model
uncertainty. While common EKF implementation assumes a constant process noise,
in real-world scenarios, the process noise varies, leading to inaccuracies in
the estimated state and potentially causing the filter to diverge. To cope with
such situations, model-based adaptive EKF methods were proposed and
demonstrated performance improvements, highlighting the need for a robust
adaptive approach. In this paper, we derive and introduce A-KIT, an adaptive
Kalman-informed transformer to learn the varying process noise covariance
online. The A-KIT framework is applicable to any type of sensor fusion. Here,
we present our approach to nonlinear sensor fusion based on an inertial
navigation system and Doppler velocity log. By employing real recorded data
from an autonomous underwater vehicle, we show that A-KIT outperforms the
conventional EKF by more than 49.5% and model-based adaptive EKF by an average
of 35.4% in terms of position accuracy.

本研究介绍了一种自适应的 Kalman-fusion 变换器 (A-KIT) 方法，通过学习实时变化的过程噪声协方差矩阵来提高传感器融合的性能，并通过实验表明，相对于传统的 EKF 和基于模型的自适应 EKF，A-KIT 在位置精度上提高了超过 49.5% 和平均 35.4%。