Marginalized particle filters for mixed linear/nonlinear state-space models
By Thomas Bo Schön, Fredrik K. Gustafsson, Per-Johan Nordlund. IEEE Transactions on Signal Processing, 2005
693 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Oceanography
View Paper: https://www.semanticscholar.org/paper/5c0007dca16a4bffd6d5499e19dfc2e34863f1f9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Simo Särkkä
- Thomas Bo Schön
- Fredrik K. Gustafsson
- Eric Moulines
- Fredrik Lindsten
- Olivier Capp'e
- Lyudmila S. Mihaylova
- Ke Wang
In Oceanography
- Simo Särkkä
- Thomas Bo Schön
- Ke Wang
- Le Yang
- Lyudmila S. Mihaylova
- Eric Moulines
- Fredrik K. Gustafsson
- Olivier Capp'e
Citations in the corpus
- Bayesian Filtering and Smoothing (2013, methodology): “We can, for example, include additional latent-variable dependent non-linear terms into the dynamic and measurement models (Schön et al., 2005).”
- Particle Filter SLAM with High Dimensional Vehicle Model (2009, methodology): “Proof The proof is derived in [32] for the case without map features but with linear Gaussian dynamics as a substructure.”
- Online Sparse Multi-Output Gaussian Process Regression and Learning (2019, methodology): “filtering framework with a mixed linear/nonlinear statespace model [46].”
- Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs (2017, background): “The Rao–Blackwellized particle filter, also known as the marginalized particle filter, was designed to achieve precisely this [2, 22].”
- In-Car Positioning and Navigation Technologies—A Survey (2009, background): “However, since the navigation equations in many navigation systems are only partial nonlinear, the filtering problem can be divided into a linear part and a nonlinear part, where the linear part, under the assumption of Gaussiandistributed noise entries, may be solved using a Kalman filter, hence, r”
- Identification of jump Markov linear models using particle filters (2014, neutral): “, [31].”
- An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, background): “Recent work [50], [51] has studied in detail the possible classes of model that may be handled by the marginalised filter, and computational complexity issues.”