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)...
Paper Details
Title
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models
Published Date
May 1, 2020
Journal
Volume
32
Issue
5
Pages
969 - 1017
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History