Particles resampling scheme using regularized optimal transport for sequential Monte Carlo filters-Reference-Cited by-同舟云学术

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Particles resampling scheme using regularized optimal transport for sequential Monte Carlo filters

Published:2018-08-02 Issue:10 Volume:32 Page:1393-1402
ISSN:0890-6327
Container-title:International Journal of Adaptive Control and Signal Processing
language:en
Short-container-title:Int J Adapt Control Signal Process

Author:

Kang Chang Ho¹,Park Chan Gook¹^ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering; Seoul National University; Gwanak-ro, Gwanak-gu Seoul 08826 South Korea

Funder

Ministry of Science, ICT and Future Planning

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Signal Processing,Control and Systems Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/acs.2918/fullpdf

Reference26 articles.

1. Bayesian Filtering and Smoothing

2. Moment conditions for convergence of particle filters with unbounded importance weights;Mbalawata;Signal Process,2016

3. Resampling methods for particle filtering: classification, implementation, and strategies;Li;IEEE Signal Process Mag,2015

4. Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming;Lu;Int J Adapt Control Signal Process,2016

5. A sigma point-based resampling algorithm in particle filter;Tang;Int J Adapt Control Signal Process,2012

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. 1-Point RANSAC UKF with Inverse Covariance Intersection for Fault Tolerance;Sensors;2020-01-08

2. Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter;IEEE Access;2020

3. Ensemble transform particle filter using regularized optimal transport and measure of nonlinearity;Measurement;2019-11

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