Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Straka Ondřej"'
Autor:
Krejčí, Jan, Kost, Oliver, Straka, Ondřej, Xia, Yuxuan, Svensson, Lennart, García-Fernández, Ángel F.
Multi-object tracking algorithms are deployed in various applications, each with unique performance requirements. For example, track switches pose significant challenges for offline scene understanding, as they hinder the accuracy of data interpretat
Externí odkaz:
http://arxiv.org/abs/2412.08321
This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is re
Externí odkaz:
http://arxiv.org/abs/2412.07239
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 deterministic-integration-rule-based point-mass method. A novel concept of reliabl
Externí odkaz:
http://arxiv.org/abs/2412.06376
Publikováno v:
Oliver Kost, Jind\v{r}ich Dun\'ik, Ond\v{r}ej Straka, Noise Covariances Identification by MDM: Weighting, Recursion, and Implementation, IFAC-PapersOnLine, Volume 58, Issue 15, 2024, Pages 342-347, ISSN 2405-8963
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 time-varying dimensions of the measurement and control. Three MDM ident
Externí odkaz:
http://arxiv.org/abs/2412.06373
This paper focuses on identification of the state noise density of a linear time-varying system described by the state-space model with the known measurement noise density. For this purpose, a novel method extending the capabilities of the measuremen
Externí odkaz:
http://arxiv.org/abs/2412.01424
Autor:
Straka, Ondřej, Havlík, Jindřich
The paper deals with measures of nonlinearity. In state estimation, they are utilized i) to select a suitable state estimation algorithm by assessing the nonlinearity of a system model, ii) to adapt the estimation algorithm structure or parameters, o
Externí odkaz:
http://arxiv.org/abs/2410.13539
Autor:
Shlezinger, Nir, Revach, Guy, Ghosh, Anubhab, Chatterjee, Saikat, Tang, Shuo, Imbiriba, Tales, Dunik, Jindrich, Straka, Ondrej, Closas, Pau, Eldar, Yonina C.
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 mathematical representations in the form of simple state-space (SS) mo
Externí odkaz:
http://arxiv.org/abs/2410.12289
This paper examines the influence of initial guesses on trajectory planning for Unmanned Aerial Vehicles (UAVs) formulated in terms of Optimal Control Problem (OCP). The OCP is solved numerically using the Pseudospectral collocation method. Our appro
Externí odkaz:
http://arxiv.org/abs/2407.01366
Building on our previous work, this paper investigates the effectiveness of interpolating control (IC) for real-time trajectory tracking. Unlike prior studies that focused on trajectory tracking itself or UAV stabilization control in simulation, we e
Externí odkaz:
http://arxiv.org/abs/2407.01095
The paper presents a mission planner for an autonomous unmanned aerial vehicle (UAV) battery management system. The objective of the system is to plan replacements of the UAV's battery on the static battery management stations. The plan ensures that
Externí odkaz:
http://arxiv.org/abs/2407.01084