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Autonomous Navigation of Mars Probes by Combining Optical Data of Viewing Martian Moons and SST Data

Published online by Cambridge University Press:  13 April 2015

Pengbin Ma ,
Fanghua Jiang  and
Hexi Baoyin
Pengbin Ma
Affiliation:
(School of Aerospace Engineering, Tsinghua University, Beijing, China) (State Key Laboratory of Astronautic Dynamics, Xi'an, China) (E-mail: [email protected])
Fanghua Jiang
Affiliation:
(School of Aerospace Engineering, Tsinghua University, Beijing, China)
Hexi Baoyin
Affiliation:
(School of Aerospace Engineering, Tsinghua University, Beijing, China)
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Abstract

Autonomous navigation has become a key technology for deep space exploration missions. Phobos and Deimos, the two natural moons of Mars, are important optical navigation information sources available for Mars missions. However, during the phase of the probe orbiting close to Mars, the ephemeris bias and the difference between the barycentre and the centre of brightness of a Martian moon will result in low navigation accuracy. On the other hand, Satellite-to-Satellite Tracking (SST) can achieve convenient and high accuracy observation for autonomous navigation. However, this cannot apply for a Mars mission during the Mars orbit phase only by SST data because of a rank defect problem of the Jacobian matrix. To improve the autonomous navigation accuracy of Mars probes, this paper presents a new autonomous navigation method that combines SST radio data provided by two probes and optical measurement by viewing the natural Martian moons. Two sequential orbit determination algorithms, an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared. Simulation results show this method can obtain high autonomous navigation accuracy during the probe's Mars Orbit phase.

Keywords

Autonomous navigationMars probeOrbit determinationDeep space exploration.
Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 6 , November 2015 , pp. 1019 - 1040
Copyright
Copyright © The Royal Institute of Navigation 2015 

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