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.
References
[1]
Kinsey, J.C.; Eustice, R.; Whitcomb, L.L. A Survey of Underwater Vehicle Navigation: Recent Advances and New Challenges. Proceedings of IFAC Conference on Maneuvering and Control of Marine Craft, Lisbon, Portugal, September 2006.
[2]
Stutters, L.; Liu, H.; Tiltman, C.; Brown, D.J. Navigation technologies for autonomous underwater vehicles. IEEE Trans. Syst. Man Cy. C 2008, 38, 581–589.
[3]
Li, W.; Wang, J.; Wu, W.; Lu, L. A fast SINS initial alignment scheme for underwater vehicle applications. J. Navig. 2012, doi:10.1017/S0373463312000318.
[4]
Silson, P.M.G. Coarse alignment of a ship's strapdown inertial attitude reference system using velocity loci. IEEE Trans. Instrum. Meas. 2011, 38, 581–589.
[5]
Gao, W.; Zhang, X.; Zhao, G.; Ben, Y. A Fine Alignment Method about Doppler-assisted SINS. Proceedings of the 2010 IEEE International Conference on Information and Automation, Harbin, China, 20–23 June 2010; pp. 2333–2337.
[6]
Ben, Y.; Zhu, Z.; Li, Q.; Wu, X. DVL Aided Fine Alignment for Marine SINS. Proceedings of the 2011 IEEE International Conference on Mechatronics and Automation, Beijing, China, 7–10 August 2011; pp. 1630–1635.
[7]
Liu, Y.; Yu, A.; Zhu, J.; Liang, D. Unscented Kalman filtering in the additive noise case. Sci. China Technol. Sc. 2010, 53, 929–941.
[8]
Kwangjin, K.; Chan, G. Non-symmetric unscented transformation with application to in-flight alignment. Int. J. Control. Autom. Syst. 2010, 8, 776–781.
[9]
Wang, Q.; Li, Y.; Rizos, C.; Li, S. The UKF and CDKF for Low-Cost SDINS/GPS in-Motion Alignment. Proceedings of International Symposium on GPS/GNSS, Yokohama, Japan; 2008; pp. 441–448.
[10]
Seo, W.; Hwang, S.; Park, J.; Lee, J. Precise outdoor localization with a GPS–INS integration system. Robotica 2012, doi:10.1017/S0263574712000379.
[11]
Shin, E. Estimation Techniques for Low-Cost Inertial Navigation. Ph.D. Thesis, Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada, May 2005.
[12]
Petovello, M.G.; Cannon, M.E.; Lachapelle, G. Kalman Filter Reliability Analysis Using Different Update Strategies. Proceedings of the CASI Annual General Meeting, Montreal, QC, Canada, 28–30 April 2003.
[13]
Shin, E. Accuracy Improvement of Low Cost INS/GPS for Land Application. M.Sc. Thesis, Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada, December 2001.
[14]
Angrisano, A.; Petovello, M.; Pugliano, G. Benefits of combined GPS/GLONASS with low-cost MEMS IMUs for vehicular urban navigation. Sensors 2012, 12, 5134–5158.
[15]
Zhang, S. An Adaptive Unscented Kalman filter for Dead Reckoning Systems. Proceedings of International conference on Information Engineering and Computer Science, Beijing, China, 19–20 December 2009; pp. 1–4.
[16]
Ding, W.; Wang, J.; Rizos, C. Improving adaptive Kalman estimation in GPS/INS integration. J. Navig. 2007, 60, 517–529.
[17]
Qu, C.; Xu, H.; Tan, Y. SINS/CNS integrated navigation solution using adaptive unscented Kalman filtering. Int. J. Comput. Appl. Technol. 2011, 41, 109–116.
[18]
Wang, J. Stochastic modeling for RTK GPS/Glonass positioning. J. Inst. Navig. 2000, 46, 297–305.
[19]
Fang, J.; Yang, S. Study on innovation adaptive EKF for in-flight Alignment of airborne POS. IEEE Trans. Instrum. Meas. 2011, 60, 1378–1388.
[20]
Kong, X.; Nebot, E.M.; Durrant-Whyte, H. Development of a Non-Linear Psi-Angle Model for Large Misalignment Errors and its Application in INS Alignment and Calibration. Proceedings of the 1999 IEEE International Conference on Robotics and Automation, Detroit, MI, USA, May 1999; pp. 1430–1435.
[21]
Kinsey, J.C.; Whitcomb, L.L. Adaptive identification on the group of rigid-body rotations and its application to underwater vehicle navigation. IEEE Trans. Robot. 2007, 23, 124–136.
[22]
Han, S.; Wang, J. A novel initial alignment scheme for low-cost INS aided by GPS for land vehicle application. J. Navig. 2010, 63, 663–680.
[23]
Xiong, K.; Zhang, H.; Chan, C. Performance evaluation of UKF-based nonlinear filtering. Automatica. 2006, 42, 261–270.
[24]
Wu, Y.; Wu, M.; Hu, D.; Hu, X. Unscented Kalman filtering for additive noise case: augmented versus non-augmented. IEEE. Signal Proc. Lett. 2005, 12, 357–360.
[25]
Van, R. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. Ph.D. Thesis, Oregon Health & Science University, Portland, April 2004.
[26]
Yan, G.; Yan, W.; Xv, D. Application of simplified UKF in SINS initial alignment for large misalignment angles. J. Chin. Inert. Technol. 2008, 16, 253–264.
[27]
Xu, J.; Jing, Y.; Dimirovski, G.; Ban, Y. Two-Stage Unscented Kalman Filter for Nonlinear Systems in the Presence of Unknown Random Bias. Proceeding of American Control Conference, Seattle, Washington USA, 11–13 June 2008; pp. 3530–3535.
[28]
Wan, E.A.; Van, R. The Unscented Kalman Filter for Nonlinear Estimation. Proceeding of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, Lake Louise, AB, Canada, 1–4 October 2000; pp. 153–158.
[29]
Almagbile, A.; Wang, J.; Ding, W. Evaluating the performance of adaptive Kalman filter methods in GPS/INS integration. CPGPS 2010, 9, 33–40.
[30]
Yang, Y.; Gao, W. An optimal adaptive Kalman filter. J. Geodesy. 2006, 80, 177–183.
