Quasi-Monte Carlo Gaussian Particle Filtering Acceleration Using CUDA

2011 ◽  
Vol 130-134 ◽  
pp. 3311-3315
Author(s):  
Nai Gao Jin ◽  
Fei Mo Li ◽  
Zhao Xing Li
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Particle Filtering ◽  
Parallel Implementation ◽  
Accurate Estimation ◽  
Quasi Monte Carlo ◽  
Speedup Ratio ◽  
Non Gaussian ◽  
Gpu Implementation ◽  
Number Of Particles

A CUDA accelerated Quasi-Monte Carlo Gaussian particle filter (QMC-GPF) is proposed to deal with real-time non-linear non-Gaussian problems. GPF is especially suitable for parallel implementation as a result of the elimination of resampling step. QMC-GPF is an efficient counterpart of GPF using QMC sampling method instead of MC. Since particles generated by QMC method provides the best-possible distribution in the sampling space, QMC-GPF can make more accurate estimation with the same number of particles compared with traditional particle filter. Experimental results show that our GPU implementation of QMC-GPF can achieve the maximum speedup ratio of 95 on NVIDIA GeForce GTX 460.

Improved Particle Filter for Integrated Navigation System

2014 ◽  
Vol 543-547 ◽  
pp. 1278-1281 ◽  
Author(s):  
Zhun Jiao ◽  
Rong Zhang
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Bayesian Filtering ◽  
Integrated Navigation ◽  
Proposal Distribution ◽  
Gaussian Distributions ◽  
Integrated Navigation System ◽  
Non Gaussian

As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved particle filter (IPF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for SINS/GPS integrated navigation system. The simulation results confirm that the improved particle filter outperforms the others.

scholarly journals Review article: Comparison of local particle filters and new implementations

2018 ◽  
Vol 25 (4) ◽  
pp. 765-807 ◽  
Author(s):  
Alban Farchi ◽  
Marc Bocquet
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Particle Filtering ◽  
Transport Theory ◽  
Optimal Transport ◽  
Curse Of Dimensionality ◽  
Local Analysis ◽  
Problem Size ◽  
Yield Data ◽  
Non Gaussian

Abstract. Particle filtering is a generic weighted ensemble data assimilation method based on sequential importance sampling, suited for nonlinear and non-Gaussian filtering problems. Unless the number of ensemble members scales exponentially with the problem size, particle filter (PF) algorithms experience weight degeneracy. This phenomenon is a manifestation of the curse of dimensionality that prevents the use of PF methods for high-dimensional data assimilation. The use of local analyses to counteract the curse of dimensionality was suggested early in the development of PF algorithms. However, implementing localisation in the PF is a challenge, because there is no simple and yet consistent way of gluing together locally updated particles across domains. In this article, we review the ideas related to localisation and the PF in the geosciences. We introduce a generic and theoretical classification of local particle filter (LPF) algorithms, with an emphasis on the advantages and drawbacks of each category. Alongside the classification, we suggest practical solutions to the difficulties of local particle filtering, which lead to new implementations and improvements in the design of LPF algorithms. The LPF algorithms are systematically tested and compared using twin experiments with the one-dimensional Lorenz 40-variables model and with a two-dimensional barotropic vorticity model. The results illustrate the advantages of using the optimal transport theory to design the local analysis. With reasonable ensemble sizes, the best LPF algorithms yield data assimilation scores comparable to those of typical ensemble Kalman filter algorithms, even for a mildly nonlinear system.

scholarly journals A uniformly convergent adaptive particle filter

2005 ◽  
Vol 42 (4) ◽  
pp. 1053-1068 ◽  
Author(s):  
Anastasia Papavasiliou
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Particle Filter ◽  
Adaptive Estimation ◽  
Optimal Filter ◽  
Simultaneous Estimation ◽  
Unknown Parameters ◽  
Partially Observed ◽  
Uniformly Convergent ◽  
Number Of Particles

Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number of particles go to infinity. The filter presented here generalizes Del Moral's Monte Carlo particle filter.

scholarly journals A Zero-Velocity Update Method for Adaptive Particle Filtering

2020 ◽  
Vol 38 (2) ◽  
pp. 427-433
Author(s):  
Yuyang Guo ◽  
Xiangbo Xu ◽  
Miaoxin Ji
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Particle Filtering ◽  
State Space Model ◽  
Classical Particle ◽  
Measurement Unit ◽  
Correction Algorithm ◽  
Zero Velocity ◽  
Non Gaussian ◽  
Navigation Errors

Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.

scholarly journals Obstacles to High-Dimensional Particle Filtering

2008 ◽  
Vol 136 (12) ◽  
pp. 4629-4640 ◽  
Author(s):  
Chris Snyder ◽  
Thomas Bengtsson ◽  
Peter Bickel ◽  
Jeff Anderson
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
The State ◽  
Ensemble Size ◽  
Problem Size ◽  
Asymptotic Results ◽  
Gaussian Analysis ◽  
Non Gaussian ◽  
State Component ◽  
Size Scales

Abstract Particle filters are ensemble-based assimilation schemes that, unlike the ensemble Kalman filter, employ a fully nonlinear and non-Gaussian analysis step to compute the probability distribution function (pdf) of a system’s state conditioned on a set of observations. Evidence is provided that the ensemble size required for a successful particle filter scales exponentially with the problem size. For the simple example in which each component of the state vector is independent, Gaussian, and of unit variance and the observations are of each state component separately with independent, Gaussian errors, simulations indicate that the required ensemble size scales exponentially with the state dimension. In this example, the particle filter requires at least 1011 members when applied to a 200-dimensional state. Asymptotic results, following the work of Bengtsson, Bickel, and collaborators, are provided for two cases: one in which each prior state component is independent and identically distributed, and one in which both the prior pdf and the observation errors are Gaussian. The asymptotic theory reveals that, in both cases, the required ensemble size scales exponentially with the variance of the observation log likelihood rather than with the state dimension per se.

A New Adaptive UPF Algorithm through Improved Relative Entropy

2013 ◽  
Vol 658 ◽  
pp. 569-573
Author(s):  
Wen Tao Yu ◽  
Jun Peng ◽  
Xiao Yong Zhang
Keyword(s):  
Particle Filter ◽  
Relative Entropy ◽  
Particle Number ◽  
Time Step ◽  
Unscented Particle Filter ◽  
Posterior Probability Density ◽  
The Difference ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Number Of Particles

Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.

An Improved Particle Filter SLAM Algorithm in Similar Environments

2014 ◽  
Vol 590 ◽  
pp. 677-682 ◽  
Author(s):  
Guo Liang Zhang ◽  
Er Liang Yao ◽  
Wen Jun Tang ◽  
Jun Xu
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
Laser Scanning ◽  
Posterior Probability ◽  
Proposal Distribution ◽  
Posterior Probability Distribution ◽  
Maximum Value ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Number Of Particles

Because of the challenging data association in similar environments, a large number of particles are needed to improve the precision in particle filtering SLAM (simultaneous localization and mapping).An improved particle filter SLAM algorithm based on particle swarm optimization in similar environments is proposed. A multimode proposal distribution is acquired by combining the information of the odometry and the laser scanning. Particles are concentrated to the region of each posterior probability distribution maximum value by PSO. The performance of the conventional particle filter SLAM is improved. The simulation experiment results prove its effectiveness and feasibility.

scholarly journals An Adaptive Unscented Particle Filter Algorithm through Relative Entropy for Mobile Robot Self-Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wentao Yu ◽  
Jun Peng ◽  
Xiaoyong Zhang ◽  
Shuo Li ◽  
Weirong Liu
Keyword(s):  
Nonlinear System ◽  
Particle Filter ◽  
State Estimation ◽  
Real Time ◽  
Relative Entropy ◽  
Gaussian Noise ◽  
The Real ◽  
Unscented Particle Filter ◽  
Non Gaussian ◽  
Number Of Particles

Self-localization is a basic skill for mobile robots in the dynamic environments. It is usually modeled as a state estimation problem for nonlinear system with non-Gaussian noise and needs the real-time processing. Unscented particle filter (UPF) can handle the state estimation problem for nonlinear system with non-Gaussian noise; however the computation of UPF is very high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF) algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.

scholarly journals State estimation and prediction using clustered particle filters

2016 ◽  
Vol 113 (51) ◽  
pp. 14609-14614 ◽  
Author(s):  
Yoonsang Lee ◽  
Andrew J. Majda
Keyword(s):  
Particle Filter ◽  
Particle Filtering ◽  
Particle Filters ◽  
Forecast Model ◽  
Coarse Grained ◽  
State Variables ◽  
High Quality ◽  
Gaussian Statistics ◽  
Non Gaussian ◽  
True Signal

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors.

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