The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.

ABSTRACT: The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation|state) is nonlinear. We argue that in many cases, a model for p(state|observation) proves both easier to learn and more accurate for latent state estimation. Approximating p(state|observation) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p(observation|state) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein-von Mises theore

SUBMITTER: Burkhart MC 

PROVIDER: S-EPMC8259355 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature