arXiv (OAI Expanded)
open access
Probe Sets for Nonlinear Kalman Filtering: Multi-Center Gains and Covariance Recalibration
Kalman filters (KFs) choose the gain as the product of the state--measurement cross-covariance and the inverse of the innovation covariance. In nonlinear systems, the predictive distribution generally changes shape, and these covariances require approximation. Conventional nonlinear KFs approximate
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Unified Frequency-Domain Model for Cascaded Filter-Interpolation Modulation in Tomographic Reconstruction#1077149
- Koopman operator modelling of non-linear dynamical systems for ensemble methods#665145
- Proper-score observation-driven filters: local geometry, estimation, and continuous-time limits#820588
- Resource-Efficient Distributed Recursive Gaussian Processes#1063190
- Toward Calibrated Mixture-of-Experts Under Distribution Shift#290563
Record · ID 1083727
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive