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Particle filter

Known as: Particle filters, Sequential importance resampling, Sequential Monte Carlo methods 
Particle filters or Sequential Monte Carlo (SMC) methods are a set of genetic-type particle Monte Carlo methodologies to solve the filtering problem… Expand
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Papers overview

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Highly Cited
2007
Highly Cited
2007
Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous… Expand
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Highly Cited
2005
Highly Cited
2005
This contribution is devoted to the comparison of various resampling approaches that have been proposed in the literature on… Expand
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Highly Cited
2004
Highly Cited
2004
The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and… Expand
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Highly Cited
2003
Highly Cited
2003
Abstract Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for… Expand
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Highly Cited
2003
Highly Cited
2003
Sequential Bayesian estimation for nonlinear dynamic state-space models involves recursive estimation of filtering and predictive… Expand
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Highly Cited
2002
Highly Cited
2002
A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is… Expand
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Highly Cited
2000
Highly Cited
2000
In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented… Expand
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Highly Cited
2000
Highly Cited
2000
Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow… Expand
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Highly Cited
2000
Highly Cited
2000
The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered… Expand
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Highly Cited
1999
Highly Cited
1999
This article analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not… Expand
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