A Novel Particle Filter for High-Dimensional Systems Using Penalized Perturbations

2019 ◽  
Author(s):  
Yousef El-Laham ◽  
Zahraa Krayem ◽  
Jessica Maghakian ◽  
Monica Bugallo
Keyword(s):  
Particle Filter ◽  
High Dimensional

scholarly journals Particle Filter SLAM with High Dimensional Vehicle Model

2009 ◽  
Vol 55 (4-5) ◽  
pp. 249-266 ◽  
Author(s):  
David Törnqvist ◽  
Thomas B. Schön ◽  
Rickard Karlsson ◽  
Fredrik Gustafsson
Keyword(s):  
Particle Filter ◽  
High Dimensional ◽  
Vehicle Model

scholarly journals A particle-filter framework for robust cryoEM 3D reconstruction

2018 ◽  
Author(s):  
Mingxu Hu ◽  
Hongkun Yu ◽  
Kai Gu ◽  
Kunpeng Wang ◽  
Siyuan Ren ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Probability Density Function ◽  
Particle Filter ◽  
Probability Density ◽  
Density Function ◽  
Sequential Monte Carlo ◽  
High Dimensional ◽  
Dimensional Parameter ◽  
Support Points ◽  
Estimated Parameters

AbstractElectron cryo-microscopy (cryoEM) is now a powerful tool in determining atomic structures of biological macromolecules under nearly natural conditions. The major task of single-particle cryoEM is to estimate a set of parameters for each input particle image to reconstruct the three-dimensional structure of the macromolecules. As future large-scale applications require increasingly higher resolution and automation, robust high-dimensional parameter estimation algorithms need to be developed in the presence of various image qualities. In this paper, we introduced a particle-filter algorithm for cryoEM, which was a sequential Monte Carlo method for robust and fast high-dimensional parameter estimation. The cryoEM parameter estimation problem was described by a probability density function of the estimated parameters. The particle filter uses a set of random and weighted support points to represent such a probability density function. The statistical properties of the support points not only enhance the parameter estimation with self-adaptive accuracy but also provide the belief of estimated parameters, which is essential for the reconstruction phase. The implementation of these features showed strong tolerance to bad particles and enabled robust defocus refinement, demonstrated by the remarkable resolution improvement at the atomic level.

scholarly journals The manifold particle filter for state estimation on high-dimensional implicit manifolds

2017 ◽  
Author(s):  
Michael C. Koval ◽  
Matthew Klingensmith ◽  
Siddhartha S. Srinivasa ◽  
Nancy S. Pollard ◽  
Michael Kaess
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
High Dimensional

scholarly journals Whole-Body Joint Angle Estimation for Real-Time Humanoid Robot Imitation Based on Gaussian Process Dynamical Model and Particle Filter

2019 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Jian Mi ◽  
Yasutake Takahashi
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Humanoid Robot ◽  
Joint Angle ◽  
Computational Cost ◽  
High Dimensional ◽  
Motion Model ◽  
Joint Angles ◽  
Compact State ◽  
Low Computational Cost

Real-time imitation enables a humanoid robot to mirror the behavior of humans, being important for applications of human–robot interaction. For imitation, the corresponding joint angles of the humanoid robot should be estimated. Generally, a humanoid robot comprises dozens of joints that construct a high-dimensional exploration space for estimating the joint angles. Although a particle filter can estimate the robot state and provides a solution for estimating joint angles, the computational cost becomes prohibitive given the high dimension of the exploration space. Furthermore, a particle filter can only estimate the joint angles accurately using a motion model. To realize accurate joint angle estimation at low computational cost, Gaussian process dynamical models (GPDMs) can be adopted. Specifically, a compact state space can be constructed through the GPDM learning of high-dimensional time-series motion data to obtain a suitable motion model. We propose a GPDM-based particle filter using a compact state space from the learned motion models to realize efficient estimation of joint angles for robot imitation. Simulations and real experiments demonstrate that the proposed method efficiently estimates humanoid robot joint angles at low computational cost, enabling real-time imitation.

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

2021 ◽  
Vol 137 ◽  
pp. 103725
Author(s):  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Héber Sobreira ◽  
José Boaventura Cunha ◽  
Armando Jorge Sousa
Keyword(s):  
Particle Filter ◽  
High Dimensional ◽  
Localization And Mapping ◽  
Mapping Systems

Motion Redirection Based on Markov Chain Monte Carlo Particle Filter

2014 ◽  
Vol 668-669 ◽  
pp. 1086-1089
Author(s):  
Jin Bao Song ◽  
Long Ye ◽  
Qin Zhang ◽  
Jian Ping Chai
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Particle Filter ◽  
Monte Carlo Sampling ◽  
Space Time ◽  
High Dimensional ◽  
Observation Model ◽  
Key Frame ◽  
The Subject

This paper aims at the difficulty that lack of observation model and high-dimensional sampling in video tooning, proposes a method based on key frame matching and dual-directional Markov chain Monte Carlo sampling of video motion redirection. At first, after extracting the key frame of a given video, By affine transformation and linear superposition, the subject initializes the video’s space-time parameters and forms the observation model; Secondly, in each space-time, based on the bi-directional Markov property of each frame, This paper proposed a dual-directional Markov chain Monte Carlo sampling particle filter structure and takes full advantage of the relationship of the front and back frame of the parameters to estimate motion redirection parameters. At the same time, for high-dimensional sampling problem, the subject according to the directional parameters’ correlation implements classification of skeleton parameters-morphological parameters-physical parameters, proposes a hierarchical genetic strategy to optimize the output parameters and improves the efficiency of the algorithm. The research of this paper will produce an efficient and prominent animation expressive video motion redirection method and play an important role on video animation of the development.

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