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High-precision Mars Entry Integrated Navigation Under Large Uncertainties

Published online by Cambridge University Press:  20 November 2013

Shuang Li ,
Xiuqiang Jiang  and
Yufei Liu
Shuang Li*
Affiliation:
(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Xiuqiang Jiang
Affiliation:
(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Yufei Liu
Affiliation:
(P.O. Box 5142-228, China Academy of Space Technology, Beijing 100094, China)
*
(E-mail: lishuang@nuaa.edu.cn)
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Abstract

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

Keywords

Mars entryIntegrated navigationDesensitised extended Kalman filterAccelerometerRadio measurement
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 2 , March 2014 , pp. 327 - 342
Copyright
Copyright © The Royal Institute of Navigation 2013 

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