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Derivative-free Nonlinear Version of Extended Recursive Three-step Filter for State and Parameter Estimation during Mars Entry

Published online by Cambridge University Press:  15 January 2018

Mengli Xiao ,
Yongbo Zhang ,
Huimin Fu  and
Zhihua Wang
Mengli Xiao
Affiliation:
(Research Center of Small Sample Technology, Beihang University, Beijing 100191, China)
Yongbo Zhang*
Affiliation:
(Research Center of Small Sample Technology, Beihang University, Beijing 100191, China)
Huimin Fu
Affiliation:
(Research Center of Small Sample Technology, Beihang University, Beijing 100191, China)
Zhihua Wang
Affiliation:
(Research Center of Small Sample Technology, Beihang University, Beijing 100191, China)
*
(E-mail: zhangyongbo@buaa.edu.cn
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Abstract

Parameter uncertainties which may lead to divergence of traditional Kalman filters during Mars entry are investigated in this paper. To achieve high precision navigation, a Derivative-free Nonlinear version of an Extended Recursive Three-Step Filter (DNERTSF) is introduced, which suits nonlinear systems with arbitrary parameter uncertainties. A DNERTSF can estimate the state and the parameters simultaneously, and Jacobian and Hessian calculations are not necessary for this filter. Considering the uncertainties in atmosphere density, ballistic coefficient and lift-to-drag ratio, a numerical simulation of Mars entry navigation is carried out. Compared with the standard Unscented Kalman Filter (UKF), DNERTSF can effectively reduce the adverse effects of parameter uncertainties and achieve a high navigation accuracy performance, keeping position and velocity estimation errors at a very low level. In all, the DNERTSF in this paper shows good advantages for Mars entry navigation, providing a possible application for a future Mars pinpoint landing.

Keywords

Nonlinear version of extended recursive three-step filterDerivative-freeParameter uncertaintiesMars entry
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 679 - 696
Copyright
Copyright © The Royal Institute of Navigation 2018 

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