Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

DOI10.1002/acs.2783zbMath1386.93283OpenAlexW2612922558MaRDI QIDQ4599751

Jindřich Havlík, Oliver Kost, Ondřej Straka, Jindřich Duník

Publication date: 4 January 2018

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.2783

zbMATH Keywords

system identificationstate estimationadaptive systemsstate-space modelnoise covariance matrix

Mathematics Subject Classification ID

Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12) Stochastic systems in control theory (general) (93E03)

Related Items (14)

The noise covariances of linear Gaussian systems with unknown inputs are not uniquely identifiable using autocovariance least-squares ⋮ Design of measurement difference autocovariance method for estimation of process and measurement noise covariances ⋮ Identification of linear systems with multiplicative noise from multiple trajectory data ⋮ System identification approach for inverse optimal control of finite-horizon linear quadratic regulators ⋮ Adaptive Kalman filtering for closed-loop systems based on the observation vector covariance ⋮ Markov chain Monte Carlo based adaptive Rauch-Tung-Striebel smoother ⋮ Kalman filters based on multibody models: linking simulation and real world. A comprehensive review ⋮ Robust linearly constrained extended Kalman filter for mismatched nonlinear systems ⋮ Noise covariance estimation via autocovariance least-squares with deadbeat filters ⋮ Process noise covariance estimation via stochastic approximation ⋮ Variational Bayesian adaptation of process noise covariance matrix in Kalman filtering ⋮ Offline state estimation for hybrid systems via nonsmooth variable projection ⋮ A multi‐tone central divided difference frequency tracker with adaptive process noise covariance tuning ⋮ Least-squares parameter estimation for state-space models with state equality constraints

Uses Software

• Matlab

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