NIRK-based accurate continuous-discrete extended Kalman filters for estimating continuous-time stochastic target tracking models

DOI10.1016/j.cam.2016.08.036zbMath1378.65143OpenAlexW2513047303MaRDI QIDQ2406644

Yanyan Li

Publication date: 5 October 2017

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2016.08.036

zbMATH Keywords

extended Kalman filteradaptive MDE solver with local and global error controlscontinuous-discrete stochastic systemMazzoni's hybrid methodcontinuous-time stochastic target tracking modelGauss- and Lobatto-type nested implicit Runge-Kutta formulas

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Numerical methods for initial value problems involving ordinary differential equations (65L05)

Related Items (5)

Optimal recursive estimation for networked stochastic uncertain systems with fading measurements and time-correlated channel noises ⋮ Stability analysis of extended, cubature and unscented Kalman filters for estimating stiff continuous-discrete stochastic systems ⋮ Numerical solution of the neural field equation in the presence of random disturbance ⋮ NIRK-based Cholesky-factorized square-root accurate continuous-discrete unscented Kalman filters for state estimation in nonlinear continuous-time stochastic models with discrete measurements ⋮ Efficient extended cubature Kalman filtering for nonlinear target tracking

Uses Software

• Matlab

Cites Work

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