Itô-Taylor-based square-root unscented Kalman filtering methods for state estimation in nonlinear continuous-discrete stochastic systems

DOI10.1016/j.ejcon.2020.07.003zbMath1458.93251OpenAlexW3043306580MaRDI QIDQ1996678

Yanyan Li

Publication date: 25 February 2021

Published in: European Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejcon.2020.07.003

zbMATH Keywords

continuous-discrete nonlinear stochastic modeldiscrete-discrete unscented Kalman filterorthogonal and \(J\)-orthogonal square-root implementationsradar trackingturning aircraft scenario with ill-conditioned measurements

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Nonlinear systems in control theory (93C10) Discrete-time control/observation systems (93C55)

Related Items (5)

Continuous-discrete unscented Kalman filtering framework by MATLAB ODE solvers and square-root methods ⋮ Robust stable iterated unscented Kalman filter based on maximum correntropy criterion ⋮ Overall hyperbolic-singular-value-decomposition-based square-root solutions in Kalman filters with deterministically sampled mean and covariance for state estimation in continuous-discrete nonlinear stochastic systems ⋮ MATLAB-based general approach for square-root extended-unscented and fifth-degree cubature Kalman filtering methods ⋮ Square-root high-degree cubature Kalman filters for state estimation in nonlinear continuous-discrete stochastic systems

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