Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment
In a non-Gaussian environment, the accuracy of a Kalman filter might be reduced. In this paper, a two- dimensional Monte Carlo Filter is proposed to overcome the challenge of the non-Gaussian environment for filtering. The two-dimensional Monte Carlo (TMC) method is first proposed to improve the eff...
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doaj-211e7e381d304955b16011ad77be379f2021-06-30T23:44:49ZengMDPI AGElectronics2079-92922021-06-01101385138510.3390/electronics10121385Two-Dimensional Monte Carlo Filter for a Non-Gaussian EnvironmentXingzi Qiang0Rui Xue1Yanbo Zhu2School of Electrical and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electrical and Information Engineering, Beihang University, Beijing 100191, ChinaAviation Data Communication Corporation, Beijing 100191, ChinaIn a non-Gaussian environment, the accuracy of a Kalman filter might be reduced. In this paper, a two- dimensional Monte Carlo Filter is proposed to overcome the challenge of the non-Gaussian environment for filtering. The two-dimensional Monte Carlo (TMC) method is first proposed to improve the efficacy of the sampling. Then, the TMC filter (TMCF) algorithm is proposed to solve the non-Gaussian filter problem based on the TMC. In the TMCF, particles are deployed in the confidence interval uniformly in terms of the sampling interval, and their weights are calculated based on Bayesian inference. Then, the posterior distribution is described more accurately with less particles and their weights. Different from the PF, the TMCF completes the transfer of the distribution using a series of calculations of weights and uses particles to occupy the state space in the confidence interval. Numerical simulations demonstrated that, the accuracy of the TMCF approximates the Kalman filter (KF) (the error is about 10<sup>−6</sup>) in a two-dimensional linear/ Gaussian environment. In a two-dimensional linear/non-Gaussian system, the accuracy of the TMCF is improved by 0.01, and the computation time reduced to 0.067 s from 0.20 s, compared with the particle filter.https://www.mdpi.com/2079-9292/10/12/1385nonlinear filternon-gaussian environmentparticle filtersequence monte carlo |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xingzi Qiang Rui Xue Yanbo Zhu |
spellingShingle |
Xingzi Qiang Rui Xue Yanbo Zhu Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment Electronics nonlinear filter non-gaussian environment particle filter sequence monte carlo |
author_facet |
Xingzi Qiang Rui Xue Yanbo Zhu |
author_sort |
Xingzi Qiang |
title |
Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment |
title_short |
Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment |
title_full |
Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment |
title_fullStr |
Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment |
title_full_unstemmed |
Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment |
title_sort |
two-dimensional monte carlo filter for a non-gaussian environment |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-06-01 |
description |
In a non-Gaussian environment, the accuracy of a Kalman filter might be reduced. In this paper, a two- dimensional Monte Carlo Filter is proposed to overcome the challenge of the non-Gaussian environment for filtering. The two-dimensional Monte Carlo (TMC) method is first proposed to improve the efficacy of the sampling. Then, the TMC filter (TMCF) algorithm is proposed to solve the non-Gaussian filter problem based on the TMC. In the TMCF, particles are deployed in the confidence interval uniformly in terms of the sampling interval, and their weights are calculated based on Bayesian inference. Then, the posterior distribution is described more accurately with less particles and their weights. Different from the PF, the TMCF completes the transfer of the distribution using a series of calculations of weights and uses particles to occupy the state space in the confidence interval. Numerical simulations demonstrated that, the accuracy of the TMCF approximates the Kalman filter (KF) (the error is about 10<sup>−6</sup>) in a two-dimensional linear/ Gaussian environment. In a two-dimensional linear/non-Gaussian system, the accuracy of the TMCF is improved by 0.01, and the computation time reduced to 0.067 s from 0.20 s, compared with the particle filter. |
topic |
nonlinear filter non-gaussian environment particle filter sequence monte carlo |
url |
https://www.mdpi.com/2079-9292/10/12/1385 |
work_keys_str_mv |
AT xingziqiang twodimensionalmontecarlofilterforanongaussianenvironment AT ruixue twodimensionalmontecarlofilterforanongaussianenvironment AT yanbozhu twodimensionalmontecarlofilterforanongaussianenvironment |
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1721350522631356416 |