A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking

By Simon Maskell, Neil J. Gordon. 2001

316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/1d2de01ccfc3418491bfb5296f9b5aa2c073e5d0

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (2011, methodology): “To cope with this issue, we adopted a numerical sampling method based on particle ﬁltering (Isard and Blake, 1998; Maskell and Gordon, 2001) to propagate and update the discretely approximated distribution over time.”
A Tutorial on Particle Filtering and Smoothing: Fifteen years later (2008, background): “As a result of the popularity of particle methods, a few tutorials have already been published on the subject [3] [8] [18] [29].”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “985-1003, ©2003 Sage Publications mate the posterior probability density over the state space of a dynamic system (Doucet, Godsill, and Andrieu 2000a; Pitt and Shephard 1999; Arulampalam et al. 2002).”
Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization (2005, background): “In Monte Carlo localization, the belief of the robot is represented by a set of random samples [26].”
Computational approaches to motor learning by imitation. (2003, unknown): “Although statistical learning has methods to uncover hidden states, e.g. by Hidden Markov Models, Kalman filters or more advanced methods (Arulampalam et al. 2002), we are not aware that such techniques have been applied to imitation yet.”
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, methodology): “These techniques are developed and extensively reviewed in [6], [7], [12], [23], [24], and [40].”
Recent advances and trends in visual tracking: A review (2011, background): “However, most real tracking problems are usually nonlinear and non-Guassian, and thus Particle Filtering [12] is proposed to deal with this situation by Monte Carlo simulation.”
Gesture Recognition: A Survey (2007, methodology): “Particle-filtering-based tracking and its applications in gesture recognition systems became popular very recently [10]–[14].”
Tracking a moving object with a binary sensor network (2003, methodology): “Our tracking algorithms have the flavor of particle filtering [1] and make three assumptions.”