<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper is focused on the nonlinear state estimation problem with finite-step correlated noises and packet loss. Firstly, by using the projection theorem...

Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

Online veröffentlicht: 02. Juni 2020

Seitenbereich: 80 - 92

Eingereicht: 10. Okt. 2019

Akzeptiert: 30. Apr. 2020

DOI: https://doi.org/10.2478/msr-2020-0011

Schlüsselwörter
Gaussian recursive filter, dropout compensations, Gaussian approximation, numerical implementation

© 2020 Li-Guo Tan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Gaussian Recursive Filter for Nonlinear Systems with Finite-step Correlated Noises and Packet Dropout Compensations

Online veröffentlicht: 02. Juni 2020

Seitenbereich: 80 - 92

Eingereicht: 10. Okt. 2019

Akzeptiert: 30. Apr. 2020

DOI: https://doi.org/10.2478/msr-2020-0011

SchlüsselwörterGaussian recursive filter, dropout compensations, Gaussian approximation, numerical implementation

© 2020 Li-Guo Tan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Gaussian recursive filter, dropout compensations, Gaussian approximation, numerical implementation