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Comparison of Several Nonlinear Filters for Mars Entry Navigation Using Radiometric Measurements

Published online by Cambridge University Press:  24 April 2017

Yuanqing Xia*
Affiliation:
(School of Automation, Beijing Institute of Technology, China) (The Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China) (The Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology, Beijing Institute of Technology, China)
Zirui Xing
Affiliation:
(School of Automation, Beijing Institute of Technology, China) (The Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China)
Liansheng Wang
Affiliation:
(School of Automation, Beijing Institute of Technology, China) (The Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, China)

Abstract

This paper studies the application of several nonlinear filters for the problem of Mars entry navigation by using radiometric measurements from Mars orbiters and Mars Surface Beacons (MSBs). A suitable dynamic model of Mars entry is developed. The movement of MSBs due to Mars rotation is also considered in the measurement model. The performance of an Extended Kalman Filter (EKF), First-order Divided Difference Filter (DDF1), Unscented Kalman Filter (UKF), and Particle Filter (PF) is compared in terms of estimation capability and computation costs. The theoretical Cramer-Rao Lower Bound (CRLB) of estimation errors are derived for Mars entry to evaluate the performance of the filters. A consistency test is also carried out to verify the filters. In simulations, by the comparison of estimation errors, position and velocity Root Mean Square Error (RMSE), error standard deviation versus Square Root of CRLB (SR CRLB), credibility and computation time, it is concluded that DDF1 is preferred for Mars entry navigation.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2017 

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