A study of the Kalman filter as a state estimator of deterministic and stochastic systems
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Publication:3050233
DOI10.1080/00207727908941630zbMath0414.93045OpenAlexW2090391200MaRDI QIDQ3050233
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Publication date: 1979
Published in: International Journal of Systems Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207727908941630
Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10)
Related Items (4)
The identification of the parameters of time-invariant stochastic systems by a method derived from the continuous-time Kalman filter ⋮ The analysis of a parameter identification algorithm which was derived from the continuous time Kalman filter ⋮ The relationship between a continuous-time identification algorithm based on the deterministic filter and least-squares methods ⋮ A Kalman filter type of extension to a deterministic gradient technique for parameter estimation
Cites Work
- A study in continuous time of the identification of initial conditions and/or parameters of deterministic system by means of a Kalman-type filter
- Minimal-order observer-estimators for continuous-time linear systems
- The structure of robust observers
- Observers for nonlinear stochastic systems
- Second-order observer for nonlinear systems from discrete noiseless measurements
- A note on Kalman-Bucy filters with zero measurement noise
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