Sensors 2013, 13(1), 1046-1063; doi:10.3390/s130101046
A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques
1
College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China
2
School of Surveying and Geospatial Engineering, University of New South Wales, Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
Received: 7 November 2012 / Revised: 25 December 2012 / Accepted: 5 January 2013 / Published: 15 January 2013
(This article belongs to the Special Issue New Trends towards Automatic Vehicle Control and Perception Systems)
Abstract
In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment. View Full-TextKeywords:
DVL-aided; in-motion alignment; AUKF; measurement noise covariance
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MDPI and ACS Style
Li, W.; Wang, J.; Lu, L.; Wu, W. A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques. Sensors 2013, 13, 1046-1063.