REVIEW 1 cited by
The geometry of low-rank Kalman filters
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
The geometry of low-rank Kalman filters
read the original abstract
An important property of the Kalman filter is that the underlying Riccati flow is a contraction for the natural metric of the cone of symmetric positive definite matrices. The present paper studies the geometry of a low-rank version of the Kalman filter. The underlying Riccati flow evolves on the manifold of fixed rank symmetric positive semidefinite matrices. Contraction properties of the low-rank flow are studied by means of a suitable metric recently introduced by the authors.
Forward citations
Cited by 1 Pith paper
-
Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics
Introduces CASSM, a computation-aware state-space model extending Kalman filtering with model selection for scale-imbalanced neural recordings, claiming competitive performance with deep networks and improved uncertai...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.