7 papers
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…
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…
Tensor Train Discrete Grid-Based Filters: Breaking the Curse of Dimensionality
J. MatouÅ¡ek, M. Brandner, J. DunÃk +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…