Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations
This paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem repeatedly, the mean and covariance of process noise and measurement noise in the condition of measurements before the current epoch are calcul...
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Online Access: | https://doi.org/10.2478/msr-2020-0011 |
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doaj-e8390df2c9e34f22834c80131b22522e2021-09-06T19:22:37ZengSciendoMeasurement Science Review1335-88712020-04-01202809210.2478/msr-2020-0011msr-2020-0011Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout CompensationsTan Li-Guo0Xu Cheng1Wang Yu-Fei2Wei Hao-Nan3Zhao Kai4Song Shen-Min5Research Center of Basic Space Science, Harbin Institute of Technology,Harbin 150001, ChinaScience and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory,Beijing 100074, ChinaBeijing Electro-mechanical Engineering Institute, Beijing 100074, ChinaBeijing Electro-mechanical Engineering Institute, Beijing 100074, ChinaControl Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaControl Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaThis paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem repeatedly, the mean and covariance of process noise and measurement noise in the condition of measurements before the current epoch are calculated. Then, based on the Gaussian approximation recursive filter (GASF) and the prediction compensation mechanism, one-step predictor and filter with packet dropouts are derived, respectively. Based on these, a nonlinear Gaussian recursive filter is proposed. Subsequently, the numerical implementation is derived based on the cubature Kalman filter (CKF), which is suitable for general nonlinear system and with higher accuracy compared to the algorithm expanded from linear system to nonlinear system through Taylor series expansion. Finally, the strong nonlinearity model is used to show the superiority of the proposed algorithm.https://doi.org/10.2478/msr-2020-0011gaussian recursive filterdropout compensationsgaussian approximationnumerical implementation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tan Li-Guo Xu Cheng Wang Yu-Fei Wei Hao-Nan Zhao Kai Song Shen-Min |
spellingShingle |
Tan Li-Guo Xu Cheng Wang Yu-Fei Wei Hao-Nan Zhao Kai Song Shen-Min Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations Measurement Science Review gaussian recursive filter dropout compensations gaussian approximation numerical implementation |
author_facet |
Tan Li-Guo Xu Cheng Wang Yu-Fei Wei Hao-Nan Zhao Kai Song Shen-Min |
author_sort |
Tan Li-Guo |
title |
Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations |
title_short |
Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations |
title_full |
Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations |
title_fullStr |
Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations |
title_full_unstemmed |
Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations |
title_sort |
gaussian recursive filter for nonlinear systems with finite-step correlated noises and packet dropout compensations |
publisher |
Sciendo |
series |
Measurement Science Review |
issn |
1335-8871 |
publishDate |
2020-04-01 |
description |
This paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem repeatedly, the mean and covariance of process noise and measurement noise in the condition of measurements before the current epoch are calculated. Then, based on the Gaussian approximation recursive filter (GASF) and the prediction compensation mechanism, one-step predictor and filter with packet dropouts are derived, respectively. Based on these, a nonlinear Gaussian recursive filter is proposed. Subsequently, the numerical implementation is derived based on the cubature Kalman filter (CKF), which is suitable for general nonlinear system and with higher accuracy compared to the algorithm expanded from linear system to nonlinear system through Taylor series expansion. Finally, the strong nonlinearity model is used to show the superiority of the proposed algorithm. |
topic |
gaussian recursive filter dropout compensations gaussian approximation numerical implementation |
url |
https://doi.org/10.2478/msr-2020-0011 |
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