Online evaluation of the process noise covariance matrix for event-based state estimators

DOI10.1002/NME.6131zbMATH Open1548.62223MaRDI QIDQ6549963

Carlos Santos, Miguel Martínez-Rey, Rubén Nieto, Felipe Espinosa, Cristina Losada

Publication date: 4 June 2024

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

zbMATH Keywords

adaptive state estimatornoise covariance matrixevent-based state estimatorprocess noise estimation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Cites Work

• On the convergence of the modified Riccati equation
• An event-triggered approach to state estimation with multiple point- and set-valued measurements
• Adaptive Kalman filtering for INS/GPS
• Event-based state estimation. A stochastic perspective
• Estimation of noise covariance matrices for a linear time-varying stochastic process
• A new autocovariance least-squares method for estimating noise covariances
• Real-time updating of structural mechanics models using Kalman filtering, modified constitutive relation error, and proper generalized decomposition
• Process Noise Covariance Design in Kalman Filtering via Bounds Optimization
• Event-Based Sensor Data Scheduling: Trade-Off Between Communication Rate and Estimation Quality
• Sequential Minimax Search for a Maximum
• Leveraging the nugget parameter for efficient Gaussian process modeling

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