The Unscented Particle Filter

By Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric A. Wan. 2000

1,778 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

Citations in the corpus

An Introduction to MCMC for Machine Learning (2004, methodology): “For instance, one can adopt suboptimal filters and other approximation methods that make use of the information available at time t to generate the proposal distribution (Doucet, Godsill, & Andrieu, 2000; de Freitas et al., 2000; Pitt & Shephard, 1999; van der Merwe et al., 2000).”
A Survey of Positioning Systems Using Visible LED Lights (2018, background): “Unlike KF, linearity and Gaussian distribution are not required in PF process [90]–[93].”
Neural Adaptive Sequential Monte Carlo (2015, methodology): “For example, the proposal could be trained using data sampled from the generative model instead, which might mitigate overfitting effects for small datasets.”
Particle Filtering in Geophysical Systems (2009, methodology): “Van der Merwe et al. (2000) use the unscented Kalman filter (UKF) as the proposal density.”
Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling (2005, background): “This effect is also known as the particle depletion problem [19].”
A particle-filtering approach for on-line fault diagnosis and failure prognosis (2009, methodology): “Neff � Nthres, a fixed threshold, a resampling algorithm (Arulampalam et al., 2002; Doucet et al., 2000; Pitt and Shephard, 1999; Van der Merwe et al., 2006 ) is performed to eliminate particles with small weights, concentrating the computational efforts in those having large ones.”
Bayesian State and Parameter Estimation of Uncertain Dynamical Systems (2006, methodology): “In the next section, we focus on stochastic simulation techniques for state estimation and use the term particle filters (PF) to denote the resulting algorithms (following the terminology of [18]), although similar algorithms have been called Monte Carlo filters [16] and sequential Monte Carlo Bayes”
Adaptive sequential Monte Carlo by means of mixture of experts (2011, methodology): “A similar approach based on the unscented Kalman filter was proposed by Van der Merwe et al. (2000) (see also Van der Merwe and Wan, 2003).”
A Tutorial on Particle Filtering and Smoothing: Fifteen years later (2008, methodology): “In particular the use of standard suboptimal filtering techniques such as the Extended Kalman Filter or the Unscented Kalman Filter to obtain importance distributions is very popular in the literature [14, 37].”
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, methodology): “Two representative algorithms in this class are the unscented Kalman filter (UKF) [11], [12] and the Gaussian quadrature Kalman filter (QKF) [13].”
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments (2002, background): “Methods for tackling this problem can be found in [60, 23, 52, 111, 138, 132].”