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Adaptive Two-stage Kalman Filter for SINS/Odometer Integrated Navigation Systems

Published online by Cambridge University Press:  28 July 2016

Hongsong Zhao ,
Lingjuan Miao  and
Haijun Shao
Hongsong Zhao*
Affiliation:
(College of Automation, Beijing Institute of Technology, China)
Lingjuan Miao
Affiliation:
(College of Automation, Beijing Institute of Technology, China)
Haijun Shao
Affiliation:
(College of Automation, Beijing Institute of Technology, China)
*
(E-mail: zh_s418@126.com)
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Abstract

In Strapdown Inertial Navigation System (SINS)/Odometer (OD) integrated navigation systems, OD scale factor errors change with roadways and vehicle loads. In addition, the random noises of gyros and accelerometers tend to vary with time. These factors may cause the Kalman filter to be degraded or even diverge. To address this problem and reduce the computation load, an Adaptive Two-stage Kalman Filter (ATKF) for SINS/OD integrated navigation systems is proposed. In the Two-stage Kalman Filter (TKF), only the innovation in the bias estimator is a white noise sequence with zero-mean while the innovation in the bias-free estimator is not zero-mean. Based on this fact, a novel algorithm for computing adaptive factors is presented. The proposed ATKF is evaluated in a SINS/OD integrated navigation system, and the simulation results show that it is effective in estimating the change of the OD scale factor error and robust to the varying process noises. A real experiment is carried out to further validate the performance of the proposed algorithm.

Keywords

Adaptive filteringInertial navigation systemsKalman filterOdometer
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 2 , March 2017 , pp. 242 - 261
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Alouani, A.T., Xia, P., Rice, T.R. and Blair, W.D. (1993). On the optimality of two-stage state estimation in the presence of random bias. IEEE Transactions on Automatic Control, 38(8), 1279–1282.Google Scholar
Ding, W.J., Li, S.L. and Xiong, W. (2006). Research on SINS/OD independently integrated navigation system for land vehicles. Metrology & Measurement Technology, 26(1), 14–16.Google Scholar
Friedland, B. (1969). Treatment of bias in recursive filtering. IEEE Transactions on Automatic Control, 14(4), 359–367.CrossRefGoogle Scholar
Fu, Q.W., Qin, Y.Y., Li, S.H. and Wang, H.M. (2012). Inertial navigation algorithm aided by motion constraints of vehicle. Journal of Chinese Inertial Technology, 20(6), 640–643.Google Scholar
Geng, Y.R. and Wang, J.L. (2008). Adaptive estimation of multiple fading factors in Kalman filter for navigation applications. GPS Solutions, 12(4), 273–279.CrossRefGoogle Scholar
Gao, W.X., Miao, L.J. and Ni, M.L. (2011). Multiple fading factors Kalman filter for SINS static alignment application. Chinese Journal of Aeronautics, 24(4), 476–483.Google Scholar
Hsieh, C.S. and Chen, F.C. (1999). Optimal solution of the two-stage Kalman estimator. IEEE Transactions on Automatic Control, 44(1), 194–199.Google Scholar
Hide, C., Moore, T. and Smith, M. (2003). Adaptive Kalman filtering for low-cost INS/GPS. Journal of Navigation, 56(1), 143–152.CrossRefGoogle Scholar
Hsieh, C.S. (2010). On the optimality of two-stage Kalman filtering for systems with unknown inputs. Asian Journal of Control, 12(4), 510–523.Google Scholar
Ignagni, M.B. (1981). An alternate derivation and extension of Friedland's two-stage Kalman estimator. IEEE Transactions on Automatic Control, 26(3), 746–750.CrossRefGoogle Scholar
Ignagni, M.B. (1990). Separate-bias Kalman estimator with bias state noise. IEEE Transactions on Automatic Control, 35(3), 338–341.Google Scholar
Ignagni, M. (2000). Optimal and suboptimal separate-bias Kalman estimators for a stochastic bias. IEEE Transactions on Automatic Control, 45(3), 547–551.CrossRefGoogle Scholar
Keller, J.Y. and Darouach, M. (1997). Optimal two-stage Kalman filter in the presence of random bias. Automatica, 33(9), 1745–1748.CrossRefGoogle Scholar
Kanev, S. and Verhaegen, M. (2005). Two-stage Kalman filtering via structured square-root. Communications in Information and Systems, 5(2), 143–168.Google Scholar
Kim, K.H., Lee, J.G. and Park, C.G. (2006). Adaptive two-stage Kalman filter in the presence of unknown random bias. International Journal of Adaptive Control Signal Processing, 20(7), 305–319.Google Scholar
Kim, K.H., Lee, J.G. and Park, C.G. (2009). Adaptive two-stage extended Kalman filter for a fault-tolerant INS-GPS loosely coupled system. IEEE Transactions in Aerospace Electronic Systems, 45(1), 125–137.Google Scholar
Mohamed, A.H. and Schwarz, K.P. (1999). Adaptive Kalman filtering for INS/GPS. Journal of Geodesy, 73(4), 193–203.CrossRefGoogle Scholar
Shi, J., Miao, L.J. and Ni, M.L. (2010). An outlier rejecting and adaptive filter algorithm applied in MEMS-SINS/GPS. Journal of Astronautics, 31(12), 2711–2716.Google Scholar
Xia, Q., Rao, M., Ying, Y. and Shen, X. (1994). Adaptive fading Kalman filter with an application. Automatica, 30(8), 1333–1338.Google Scholar
Xiao, X., Wang, Q.Z., Cheng, Y., Fu, M.Y. and Liu, T. (2012). High accuracy navigation algorithm for tightly coupled INS/Odometer. Acta Armamentarii, 33(4), 395–400.Google Scholar
Yan, G.M. (2006). Research on autonomous position and azimuth determining systems for land vehicles. Northwestern Polytechnical University, Xi'an, China.Google Scholar
Zhao, H.S., Miao, L.J. and Shen, J. (2014). High accuracy algorithm for SINS/Odometer integrated navigation system. Acta Armamentarii, 35(4), 433–440.Google Scholar