DYNAMICAL VARIATIONAL AUTOENCODERS AND KALMANNET: NEW APPROACHES TO ROBUST HIGH-PRECISION NAVIGATION

Abstract

Kalman filters, recognized as a traditional and effective inference algorithm based on state space models (SSM), have been extensively applied in the fields of navigation and mapping. However, their performance will degrade when facing model assumption mismatches, such as non-linear dynamics and non-Gaussian correlated noises. The model-based deep learning methods overcome these mismatches by combining the domain knowledge of the model-based methods and the expressiveness of the data-driven deep learning methods, and thus can provide a promising solution for addressing high-dimensional and nonlinear challenges. This paper presents a succinct overview of the principles, inference model, and training methodology employed in model-based deep learning methods, with particular focus on the KalmanNet and the Dynamical Variational Autoencoder (DVAE). Furthermore, it implements KalmanNet on robust and high-precision navigation and positioning problem. The experimental results substantiate the feasibility of achieving navigation and positioning accuracy comparable to that of the Extended Kalman Filter (EKF), while simultaneously exhibiting enhanced robustness, albeit at the cost of some computational overhead.