Optimal filtering for systems with unknown inputs via the descriptor Kalman filtering method

DOI10.1016/j.automatica.2011.08.012zbMath1228.93119OpenAlexW1976192743MaRDI QIDQ642650

Hsieh, Chien-Shu

Publication date: 27 October 2011

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2011.08.012

zbMATH Keywords

maximum likelihood estimationunknown inputsdescriptor Kalman filteringglobally optimal filteringunbiased minimum-variance filter

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10)

Related Items (7)

Simultaneous input and state estimation for nonlinear systems with applications to flow field estimation ⋮ On existence, optimality and asymptotic stability of the Kalman filter with partially observed inputs ⋮ State estimation for stochastic discrete-time systems with multiplicative noises and unknown inputs over fading channels ⋮ Distributed consensus-based estimation with unknown inputs and random link failures ⋮ Robust deterministic least-squares filtering for uncertain time-varying nonlinear systems with unknown inputs ⋮ State estimation for descriptor systems via the unknown input filtering method ⋮ Globally optimal sequential and distributed fusion state estimation for multi-sensor systems with cross-correlated noises

Cites Work

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• Optimal recursive estimation for discrete-time descriptor systems

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