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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… 
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Related topics

Related topics

49 relations
Augmented realityAuxiliary particle filterBioinformaticsComputational physics

Broader (4)

Computational statisticsControl theoryEstimation theoryRobot control

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
A Reliability-Augmented Particle Filter for Magnetic Fingerprinting Based Indoor Localization on Smartphone
Using magnetic field data as fingerprints for smartphone indoor positioning has become popular in recent years. Particle filter… 
Highly Cited
2010
Highly Cited
2010
A Kalman/Particle Filter-Based Position and Orientation Estimation Method Using a Position Sensor/Inertial Measurement Unit Hybrid System
This paper presents a novel methodology that estimates position and orientation using one position sensor and one inertial… 
Highly Cited
2009
Highly Cited
2009
A particle-filtering approach for on-line fault diagnosis and failure prognosis
This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and failure prognosis in non-linear… 
Highly Cited
2009
Highly Cited
2009
Robust Video Stabilization Based on Particle Filter Tracking of Projected Camera Motion
Video stabilization is an important technique in digital cameras. Its impact increases rapidly with the rising popularity of… 
Highly Cited
2009
Highly Cited
2009
Kalman Particle Filter for lane recognition on rural roads
Despite the availability of lane departure and lane keeping systems for highway assistance, unmarked and winding rural roads… 
Highly Cited
2009
Highly Cited
2009
A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment
Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past… 
Highly Cited
2005
Highly Cited
2005
Vision-based SLAM using the Rao-Blackwellised Particle Filter
We consider the problem of Simultaneous Localization and Mapping (SLAM) from a Bayesian point of view using the Rao-Blackwellised… 
Highly Cited
2002
Highly Cited
2002
Particle filters for state-space models with the presence of unknown static parameters
Particle filters for dynamic state-space models handling unknown static parameters are discussed. The approach is based on… 
Highly Cited
2002
Highly Cited
2002
Tracking multiple objects with particle filtering
We address the problem of multitarget tracking (MTT) encountered in many situations in signal or image processing. We consider… 
Highly Cited
2001
Highly Cited
2001
A particle filter for track-before-detect
  • D. SalmondH. Birch
  • Proceedings of the American Control Conference…
  • 2001
  • Corpus ID: 52026671
A Bayesian track-before-detect particle filter is proposed. The filter provides a sample based approximation to the distribution… 
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