Collaborative linear dynamical system identification by scarce relevant/irrelevant observations

DOI10.1007/s11045-018-0562-8zbMath1405.93075OpenAlexW2793784338MaRDI QIDQ1710964

Hadi Sadoghi Yazdi, Behzad Bakhtiari

Publication date: 24 January 2019

Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11045-018-0562-8

zbMATH Keywords

linear dynamical system identificationmulti-sensor multi-dimensional observationsrelevant/irrelevant observationsscarce observationsstate-space model parameter estimationunreliable observations

Mathematics Subject Classification ID

System identification (93B30) Linear systems in control theory (93C05) Observability (93B07) Software, source code, etc. for problems pertaining to systems and control theory (93-04)

Uses Software

• astsa

Cites Work

• Unnamed Item
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Recursive parameter estimation algorithms and convergence for a class of nonlinear systems with colored noise
• Estimation in multisensor networked systems with scarce measurements and time varying delays
• Interpolation of signals with missing data using principal component analysis
• EM-based identification of continuous-time ARMA models from irregularly sampled data
• Parameter estimation with scarce measurements
• Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering
• HOSVD based multidimensional parameter estimation for massive MIMO system from incomplete channel measurements
• The data filtering based generalized stochastic gradient parameter estimation algorithms for multivariate output-error autoregressive systems using the auxiliary model
• Time series AR modeling with missing observations based on the polynomial transformation
• Recursive parameter identification of the dynamical models for bilinear state space systems
• Subspace identification for closed-loop 2-D separable-in-denominator systems
• A flexible state-space model for learning nonlinear dynamical systems
• Robust state estimation for two-dimensional stochastic time-delay systems with missing measurements and sensor saturation
• Robust maximum-likelihood estimation of multivariable dynamic systems
• Least-squares parameter estimation for systems with irregularly missing data
• Bayesian Robust PCA for Incomplete Data
• Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms

This page was built for publication: Collaborative linear dynamical system identification by scarce relevant/irrelevant observations