Random weighting estimation of kinematic model error for dynamic navigation
- Resource Type
- Periodical
- Authors
- Zhong, Y.; Gao, S.; Wei, W.; Gu, C.; Subic, A.
- Source
- IEEE Transactions on Aerospace and Electronic Systems IEEE Trans. Aerosp. Electron. Syst. Aerospace and Electronic Systems, IEEE Transactions on. 51(3):2248-2259 Jul, 2015
- Subject
- Aerospace
Robotics and Control Systems
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Kinematics
Estimation
Systematics
Covariance matrices
Noise
Robustness
Navigation
- Language
- ISSN
- 0018-9251
1557-9603
2371-9877
This paper presents a new random weighting method to deal with the systematic error of the kinematic model for dynamic navigation. This method incorporates random weights in the kinematic model to control the systematic error of the kinematic model for improving the navigation accuracy. A theory of random weighting estimation is established, showing that 1) the random weighting estimation of the kinematic model’s systematic error is unbiased and 2) the covariance matrix of the predicted state vector can be controlled by adjusting the covariance matrices of the predicted residual vector and estimated state vector to improve the accuracy of state prediction. Random weighting estimations are also constructed for the systematic error of the kinematic model as well as the covariance matrices of predicted residual vector, predicted state vector, and state noise vector. Experimental results demonstrate the effectiveness of the proposed random weighting method in resisting the disturbances of the kinematic model noise for improving the accuracy of dynamic navigation.