Identification of State and Measurement Noise Covariance Matrices using Nonlinear Estimation Framework

12th European Workshop on Advanced Control and Diagnosis

Kost, Oliver^1 ; Straka, Ondej^1 ; Duník, Jindich^1

Department of Cybernetics, European Centre of Excellence - NTIS, University of West Bohemia, Univerzitní 8, Pilsen

306 14, Czech Republic^1

关键词: Implementation;    Least squares methods;    Noise covariance;    Non-linear estimation;    Software toolbox;    State;    space models;    State estimation algorithms;    Stochastic dynamic systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012057/pdf
DOI  :  10.1088/1742-6596/659/1/012057

来源: IOP

【 摘 要 】

The paper deals with identification of the noise covariance matrices affecting the linear system described by the state-space model. In particular, the stress is laid on the autocovariance least-squares method which belongs into to the class of the correlation methods. The autocovariance least-squares method is revised for a general linear stochastic dynamic system and is implemented within the publicly available MATLAB toolbox Nonlinear Estimation Framework. The toolbox then offers except of a large set of state estimation algorithms for prediction, filtering, and smoothing, the integrated easy-to-use method for the identification of the noise covariance matrices. The implemented method is tested by a thorough Monte-Carlo simulation for various user-defined options of the implemented method.