On the Projective Geometry of Kalman Filter

31 Mar 2015  ·  Francesca Paola Carli, Rodolphe Sepulchre ·

Convergence of the Kalman filter is best analyzed by studying the contraction of the Riccati map in the space of positive definite (covariance) matrices. In this paper, we explore how this contraction property relates to a more fundamental non-expansiveness property of filtering maps in the space of probability distributions endowed with the Hilbert metric... This is viewed as a preliminary step towards improving the convergence analysis of filtering algorithms over general graphical models. read more

PDF Abstract
No code implementations yet. Submit your code now



  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here