Random Weighting-Based Nonlinear Gaussian Filtering
The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering...
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doaj-21f5407b437d487490b222a4930a0b632021-03-30T02:52:04ZengIEEEIEEE Access2169-35362020-01-018195901960510.1109/ACCESS.2020.29683638964403Random Weighting-Based Nonlinear Gaussian FilteringZhaohui Gao0https://orcid.org/0000-0003-1952-7390Chengfan Gu1https://orcid.org/0000-0002-3510-0162Jiahui Yang2https://orcid.org/0000-0002-2048-1855Shesheng Gao3https://orcid.org/0000-0002-7980-9085Yongmin Zhong4https://orcid.org/0000-0002-0105-9296School of Automatics, Northwestern Polytechnical University, Xi’an, China(Independent Researcher), Rowville, VIC, AustraliaSchool of Automatics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Automatics, Northwestern Polytechnical University, Xi’an, ChinaSchool of Engineering, RMIT University, Melbourne, VIC, AustraliaThe Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy.https://ieeexplore.ieee.org/document/8964403/Nonlinear system state estimationGaussian filteringsystem noise characteristicsrandom weighting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhaohui Gao Chengfan Gu Jiahui Yang Shesheng Gao Yongmin Zhong |
spellingShingle |
Zhaohui Gao Chengfan Gu Jiahui Yang Shesheng Gao Yongmin Zhong Random Weighting-Based Nonlinear Gaussian Filtering IEEE Access Nonlinear system state estimation Gaussian filtering system noise characteristics random weighting |
author_facet |
Zhaohui Gao Chengfan Gu Jiahui Yang Shesheng Gao Yongmin Zhong |
author_sort |
Zhaohui Gao |
title |
Random Weighting-Based Nonlinear Gaussian Filtering |
title_short |
Random Weighting-Based Nonlinear Gaussian Filtering |
title_full |
Random Weighting-Based Nonlinear Gaussian Filtering |
title_fullStr |
Random Weighting-Based Nonlinear Gaussian Filtering |
title_full_unstemmed |
Random Weighting-Based Nonlinear Gaussian Filtering |
title_sort |
random weighting-based nonlinear gaussian filtering |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics. However, it is difficult to satisfy this condition in engineering practice, making the Gaussian filtering solution deviated or diverged. This paper adopts the random weighting concept to address the limitation of the nonlinear Gaussian filtering. It establishes the random weighting estimations of system noise characteristics on the basis of the maximum a-posterior theory, and further develops a new Gaussian filtering method based on the random weighting estimations to restrain system noise influences on system state estimation by adaptively adjusting the random weights of system noise characteristics. Simulation, experimental and comparison analyses prove that the proposed method overcomes the limitation of the traditional Gaussian filtering in requirement of system noise characteristics, leading to improved estimation accuracy. |
topic |
Nonlinear system state estimation Gaussian filtering system noise characteristics random weighting |
url |
https://ieeexplore.ieee.org/document/8964403/ |
work_keys_str_mv |
AT zhaohuigao randomweightingbasednonlineargaussianfiltering AT chengfangu randomweightingbasednonlineargaussianfiltering AT jiahuiyang randomweightingbasednonlineargaussianfiltering AT sheshenggao randomweightingbasednonlineargaussianfiltering AT yongminzhong randomweightingbasednonlineargaussianfiltering |
_version_ |
1724184386091352064 |