Noise covariance identification for nonlinear systems using expectation maximization and moving horizon estimation

DOI10.1016/j.automatica.2016.11.011zbMath1355.93192OpenAlexW2570958925MaRDI QIDQ510145

Eric C. Kerrigan, Ming Ge

Publication date: 16 February 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2016.11.011

zbMATH Keywords

nonlinear systemstate estimationextended Kalman filterexpectation maximizationmoving horizon estimationfull information estimationnoise covariance estimation

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Semidefinite programming (90C22) Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12)

Related Items (4)

The noise covariances of linear Gaussian systems with unknown inputs are not uniquely identifiable using autocovariance least-squares ⋮ Noise covariance estimation via autocovariance least-squares with deadbeat filters ⋮ Suboptimal receding horizon estimation via noise blocking ⋮ Moving horizon estimation for uncertain networked control systems with packet loss

Uses Software

• SDPT3
• SOCS
• astsa

Cites Work

• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Estimation of the disturbance structure from data using semidefinite programming and optimal weighting
• On the convergence properties of the EM algorithm
• Practical Methods for Optimal Control and Estimation Using Nonlinear Programming
• SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
• Observer design for nonlinear systems with discrete-time measurements
• A Fresh Look at the Kalman Filter
• Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations
• Noise covariance identification for time-varying and nonlinear systems
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