On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Statistics and Computing
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
Monte Carlo Filter in Mobile Robotics Localization: A Clustered Evolutionary Point of View
Journal of Intelligent and Robotic Systems
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
Interacting sequential Monte Carlo samplers for trans-dimensional simulation
Computational Statistics & Data Analysis
An Introduction to Quantum Filtering
SIAM Journal on Control and Optimization
Multi-dimensional visual tracking using scatter search particle filter
Pattern Recognition Letters
Adaptive Dynamic Clustered Particle Filtering for Mobile Robots Global Localization
Journal of Intelligent and Robotic Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive methods for sequential importance sampling with application to state space models
Statistics and Computing
Tackling the premature convergence problem in Monte-Carlo localization
Robotics and Autonomous Systems
Machine condition prognosis based on sequential Monte Carlo method
Expert Systems with Applications: An International Journal
An ant stochastic decision based particle filter and its convergence
Signal Processing
Bring consciousness to mobile robot being localized
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Expert Systems with Applications: An International Journal
Efficient particle filtering via sparse kernel density estimation
IEEE Transactions on Image Processing
Enhancing particle swarm optimization based particle filter tracker
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Multi-cue-based CamShift guided particle filter tracking
Expert Systems with Applications: An International Journal
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A PSO Accelerated Immune Particle Filter for Dynamic State Estimation
CRV '11 Proceedings of the 2011 Canadian Conference on Computer and Robot Vision
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Risk-Sensitive Particle Filters for Mitigating Sample Impoverishment
IEEE Transactions on Signal Processing - Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle Filtering for Large-Dimensional State Spaces With Multimodal Observation Likelihoods
IEEE Transactions on Signal Processing - Part I
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Decentralized Particle Filter With Arbitrary State Decomposition
IEEE Transactions on Signal Processing
Hi-index | 12.05 |
During the last two decades there has been a growing interest in Particle Filtering (PF). However, PF suffers from two long-standing problems that are referred to as sample degeneracy and impoverishment. We are investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices. These methods benefit from such methods as Markov Chain Monte Carlo methods, Mean-shift algorithms, artificial intelligence algorithms (e.g., Particle Swarm Optimization, Genetic Algorithm and Ant Colony Optimization), machine learning approaches (e.g., clustering, splitting and merging) and their hybrids, forming a coherent standpoint to enhance the particle filter. The working mechanism, interrelationship, pros and cons of these approaches are provided. In addition, approaches that are effective for dealing with high-dimensionality are reviewed. While improving the filter performance in terms of accuracy, robustness and convergence, it is noted that advanced techniques employed in PF often causes additional computational requirement that will in turn sacrifice improvement obtained in real life filtering. This fact, hidden in pure simulations, deserves the attention of the users and designers of new filters.