8 citations · 34 across the 19 of their papers we have counts for
19 papers
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. Matoušek, J. Krejčí, J. Duník +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
Jindřich Duník, Jan Krejčí, Jakub Matoušek +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…
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…
Interpretable Augmented Physics-Based Model for Estimation and Tracking
Ondřej Straka, Jindřich Duník, Pau Closas +1
State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an vaccurate dynamical model for complex scenarios or adapting to changes…
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…
Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
J. Matoušek, M. Brandner, J. Duník +1
This paper deals with the state estimation of stochastic systems and examines the possible employment of tensor decompositions in grid-based filtering routines, in particular, the…
Efficient Spectral Differentiation in Grid-Based Continuous State Estimation
Jakub Matousek, Jindrich Dunik, Marek Brandner
This paper deals with the state estimation of stochastic models with continuous dynamics. The aim is to incorporate spectral differentiation methods into the solution to the Fokker…
Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
Jindrich Dunik, Jakub Matousek, Ondrej Straka +3
This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of t…
Data-Augmented Numerical Integration in State Prediction: Rule Selection
Jindrich Dunik, Ladislav Kral, Jakub Matousek +2
This paper deals with the state prediction of nonlinear stochastic dynamic systems. The emphasis is laid on a solution to the integral Chapman-Kolmogorov equation by a deterministi…
Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation
Oliver Kost, Jindrch Dunik, Ondrej Straka
The problem of noise covariance matrix identification of stochastic linear time-varying state-space models is addressed. The measurement difference method (MDM) is generalized to t…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Pedestrian Tracking with Monocular Camera using Unconstrained 3D Motion Model
Jan Krejčí, Oliver Kost, Ondřej Straka +1
A first-principle single-object model is proposed for pedestrian tracking. It is assumed that the extent of the moving object can be described via known statistics in 3D, such as p…
Advanced Receiver Autonomous Integrity Monitoring: Impact of Time-Correlated Pseudorange Measurement Noise
Jindrich Dunik, Martin Orejas
The paper deals with the allocation of the probability of false alert within the advanced receiver integrity monitoring method. Namely, the stress is laid on the correct computatio…
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…
Online Learning and Control for Data-Augmented Quadrotor Model
Matej Smid, Jindrich Dunik
The ability to adapt to changing conditions is a key feature of a successful autonomous system. In this work, we use the Recursive Gaussian Processes (RGP) for identification of th…
Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
J. Matoušek, J. Duník, M. Brandner
This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state pr…
Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics
Jakub Matousek, Jindrich Dunik, Marek Brandner +2
This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state pr…
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
Tales Imbiriba, Ahmet Demirkaya, Jindřich Duník +3
In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows…