Repairability of Inexact Solvers in Recursive State Estimation with Machine Learning
Recursive state estimation often executes approximate numerical solutions inside a feedback loop, where highly accurate local steps do not guarantee better overall results. For a fixed linear Kalman model, we characterize when a correction within a prescribed subspace and norm budget can meet a local admissibility tolerance, and how the defects actually executed affect the finite-horizon covariance response. Centering each defect on the exact gain for the implemented covariance separates current solve error from inherited gain drift. Expanding the exact residual-drift identity reveals opposing quartic contributions beyond the quadratic response: innovation-covariance inflation enters posi...
| Authors | Yanjun Ji, Dennis Willsch, Orkun Şensebat et al. |
|---|---|
| Primary category | quant-ph |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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