Particle Filtering (PF) methods are an established class of procedures for performing
inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …

By approximating posterior distributions with weighted samples, particle filters (PFs) provide
an efficient mechanism for solving non-linear sequential state estimation problems. While …

The usage of techniques of the artificial neural networks (ANNs) in the field of microwave
devices has recently increased. The advantages of ANNs in comparison with traditional full …

This paper introduces a novel state estimation framework for robots using differentiable
ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble …

It has been widelydocumented that the sampling and resampling steps in particle filters
cannot be differentiated. The reparameterisation trick was introduced to allow the sampling …

Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and
measurement models by learning from observed data. However, most existing differentiable …

Abstract Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role
in various applications, including navigation, transportation, logistics, mapping, and …

Differentiable particle filters are an emerging class of particle filtering methods that use
neural networks to construct and learn parametric state-space models. In real-world …

Particle filters (PFs) have been widely used in speaker tracking due to their capability in
modeling a non-linear process or a non-Gaussian environment. However, particle filters are …

Map matching has been widely used in various indoor localization technologies. However,
conventional map matching technologies based on probabilistic models, such as particle …