Journal ArticleDOI
Methods for Estimating State and Measurement Noise Covariance Matrices: Aspects and Comparison
Reads0
Chats0
TLDR
The aim of the paper is to analyse identifiability of state noise parameters by means of the Bayesian approach and to summarise and compare the novel methods from both theoretical and numerical point of view.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2009-01-01. It has received 48 citations till now. The article focuses on the topics: Estimation of covariance matrices & Covariance function.read more
Citations
More filters
Journal ArticleDOI
Noise covariance matrices in state-space models: A survey and comparison of estimation methods—Part I
TL;DR: This paper deals with the estimation of the noise covariance matrices of systems described by state‐space models and a simulation comparison using exemplary MATLAB implementations of the methods is provided.
Journal ArticleDOI
Robust adaptive unscented Kalman filter for attitude estimation of pico satellites
TL;DR: In this paper, a fault-tolerant attitude estimation algorithm for pico satellites is proposed, which uses a robust adaptive unscented Kalman filter (UKF) which performs correction for the process noise covariance (Q-adaptation) or measurement noise covariances (R-adaptance) depending on the type of the fault.
Journal ArticleDOI
Robust Kalman filtering for small satellite attitude estimation in the presence of measurement faults
TL;DR: A Robust Kalman filtering method is proposed for the attitude estimation problem and two new algorithms, which are robust against measurement malfunctions, are called Robust Extended Kalman Filter and Robust Unscented Kalman filter, respectively.
Proceedings ArticleDOI
Probabilistic Assessment of the Process-Noise Covariance Matrix of Discrete Kalman Filter State Estimation of Active Distribution Networks
TL;DR: In this article, the authors present different approaches that allow assessing the optimal values of the elements composing the process noise covariance matrix within the context of the state estimation (SE) of Active Distribution Networks (ADNs).
Journal ArticleDOI
Adaptive Fading UKF with Q-Adaptation: Application to Picosatellite Attitude Estimation
TL;DR: This study introduces a novel adaptive fading UKF algorithm based on the correction of process noise covariance (Q-adaptation) for the case of mismatches with the model.
References
More filters
Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
TL;DR: With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory.
Book
Subspace Identification for Linear Systems: Theory - Implementation - Applications
Peter Van Overschee,Bart De Moor +1 more
TL;DR: This book focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finitedimensional dynamical systems, which allow for a fast, straightforward and accurate determination of linear multivariable models from measured inputoutput data.
Journal ArticleDOI
Kronecker products and matrix calculus in system theory
TL;DR: In this article, a review of the algebras related to Kronecker products is presented, which have several applications in system theory including the analysis of stochastic steady state.
Journal ArticleDOI
On the identification of variances and adaptive Kalman filtering
TL;DR: In this paper, it was shown that the steady-state optimal Kalman filter gain depends only on n \times r linear functionals of the covariance matrix and the number of unknown elements in the matrix.