`Mathematical Problems in EngineeringVolume 2012, Article ID 578719, 16 pageshttp://dx.doi.org/10.1155/2012/578719`
Research Article

## Dual-EKF-Based Real-Time Celestial Navigation for Lunar Rover

1Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
2Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
3The Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
4School of Information Science and Technology, East China Normal University, Shanghai 200241, China

Received 27 December 2011; Accepted 14 February 2012

Academic Editor: Carlo Cattani

Copyright © 2012 Li Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

A key requirement of lunar rover autonomous navigation is to acquire state information accurately in real-time during its motion and set up a gradual parameter-based nonlinear kinematics model for the rover. In this paper, we propose a dual-extended-Kalman-filter- (dual-EKF-) based real-time celestial navigation (RCN) method. The proposed method considers the rover position and velocity on the lunar surface as the system parameters and establishes a constant velocity (CV) model. In addition, the attitude quaternion is considered as the system state, and the quaternion differential equation is established as the state equation, which incorporates the output of angular rate gyroscope. Therefore, the measurement equation can be established with sun direction vector from the sun sensor and speed observation from the speedometer. The gyro continuous output ensures the algorithm real-time operation. Finally, we use the dual-EKF method to solve the system equations. Simulation results show that the proposed method can acquire the rover position and heading information in real time and greatly improve the navigation accuracy. Our method overcomes the disadvantage of the cumulative error in inertial navigation.