15 papers
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
Yehonatan Dahan, Guy Revach, Jindrich Dunik +1
Recent years have witnessed a growing interest in tracking algorithms that augment Kalman Filters (KFs) with Deep Neural Networks (DNNs). By transforming KFs into trainable deep le…
Identification of Clock Ensemble Noise Parameters Using Differential Measurement Analysis
Jindrich Dunik, Ladislav Kral, Ivo Puncochar +4
The paper addresses the critical problem of identifying unknown parameters of an atomic clock ensemble. The ensemble model is considered as a set of individual clock models, where…
Tensor Decompositions for Online Grid-Based Terrain-Aided Navigation
J. MatouÅ¡ek, J. KrejÄÃ, J. DunÃk +1
This paper presents a practical and scalable grid-based state estimation method for high-dimensional models with invertible linear dynamics and with highly non-linear measurements,…
Lagrangian Grid-based Estimation of Nonlinear Systems with Invertible Dynamics
JindÅich DunÃk, Jan KrejÄÃ, Jakub MatouÅ¡ek +2
This paper deals with the state estimation of non-linear and non-Gaussian systems with an emphasis on the numerical solution to the Bayesian recursive relations. In particular, thi…
Unobservable Systems: No Problem for Noise Identification
Oliver Kost, Jindrich Dunik, Ivo Puncochar +1
This paper deals with the noise identification of a linear time-varying stochastic dynamic system described by the state-space model. In particular, the stress is laid on the desig…
Stone Soup: ADS-B-based Multi-Target Tracking with Stochastic Integration Filter
John Hiles, Jakub Matousek, Erik Blasch +3
This paper focuses on the multi-target tracking using the Stone Soup framework. In particular, we aim at evaluation of two multi-target tracking scenarios based on the simulated cl…