Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs

DOI10.1016/j.cma.2020.113411zbMath1506.62376OpenAlexW3089711239MaRDI QIDQ2021040

Jianqi Yu, Huiping Zhu, James L. Beck, Hui Li, Yong Huang

Publication date: 26 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.113411

zbMATH Keywords

Kalman filtersparse Bayesian learningBayesian system identificationdual sequential state and parameter estimationinput time-history identificationonline model error and noise identification

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items

Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter ⋮ Bayesian structural identification using Gaussian process discrepancy models

Uses Software

• PDCO

Cites Work

• Unnamed Item
• Adaptive Kalman filtering for INS/GPS
• Estimation of the disturbance structure from data using semidefinite programming and optimal weighting
• Bayesian learning for neural networks
• A marginalized unscented Kalman filter for efficient parameter estimation with applications to finite element models
• Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment
• Stochastic processes and filtering theory
• Atomic Decomposition by Basis Pursuit
• Information Theory and Statistical Mechanics
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• 10.1162/15324430152748236
• Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms
• Compressed sensing