A local sigma-point unscented Kalman filter for geophysical data assimilation

DOI10.1016/J.PHYSD.2021.132979zbMath1490.62291OpenAlexW3174433883MaRDI QIDQ2077736

Youmin Tang, Manoj K. Nambiar, Ziwang Deng

Publication date: 21 February 2022

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2021.132979

zbMATH Keywords

Kalman filterdata assimilationnonlinear estimationunscented transformsigma point Kalman filter

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Filtering in stochastic control theory (93E11)

Cites Work

• Highly efficient sigma point filter for spacecraft attitude and rate estimation
• Ensemble Kalman filter with the unscented transform
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• Singular vectors, predictability and ensemble forecasting for weather and climate
• Approximation of attractors, large eddy simulations and multiscale methods
• Reduced-rank unscented Kalman filtering using Cholesky-based decomposition
• Gaussian filters for nonlinear filtering problems
• New developments in state estimation for nonlinear systems

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