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eess.SP2025

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…

eess.SP2025

Dual Approach to Inverse Covariance Intersection Fusion

Jiří Ajgl, Ondřej Straka

Linear fusion of estimates under the condition of no knowledge of correlation of estimation errors has reached maturity. On the other hand, various cases of partial knowledge are s…

eess.SP2025

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…

eess.SP2025

Aspects of density approximation by tensor trains

Jiří Ajgl, Ondřej Straka

Point-mass filters solve Bayesian recursive relations by approximating probability density functions of a system state over grids of discrete points. The approach suffers from the…

eess.SP2025

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…

eess.SP2025

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…