Land-Vehicle INS/GPS Accurate Positioning during GPS Signal Blockage Periods
Publication: Journal of Surveying Engineering
Volume 133, Issue 3
Abstract
In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In this paper, the main objective is to improve the accuracy of the obtained navigation parameters during periods of GPS signal outages using different bridging methods. As a first step, three different KF approaches will be used, including the linearized, extended, and unscented KF algorithms for the INS/GPS integration. Two land-vehicle kinematic data sets with different-quality INSs are used with several induced GPS outages, and then two bridging approaches are implemented. The first method is to apply different backward smoothing algorithms postmission that are associated with the different used KF approaches. The second bridging method is a near real-time approach based on developing an INS error model to be applied only during GPS signal blockages. After applying each bridging method, the results showed remarkable improvement of position errors regardless of the KF used.
Get full access to this article
View all available purchase options and get full access to this article.
References
Brown, R. G., and Hwang, P. Y. C. (1997). Introduction to random signals and applied Kalman filtering, Wiley, New York.
Crassidis, J. L., and Markley, F. L. (2003). “Unscented filtering for spacecraft attitude estimation.” J. Guid. Control Dyn., 26(4), 536–542.
Czerniak, R. J., and Reilly, J. P. (1998). “Application of GPS for surveying and other positioning needs in departments of transportation.” Synthesis of highway practice, 258, National Academy Press, Washington, D.C.
El-Sheimy, N. (2004). “Inertial techniques and INS/DGPS integration.” Notes for ENGO 623, Dept. of Geomatics Engineering, Univ. of Calgary, Alberta, Canada.
Gelb A., ed. (1974). Applied optimal estimation, MIT Press, Cambridge, Mass.
Gonthier, M. (1984). “Smoothing procedures for inertial survey systems of local level type.” Ph.D. thesis, Dept. of Civil Engineering, Div. of Surveying Engineering, Univ. of Calgary, Alberta, Canada.
Jansson, P. (1998). “Precise kinematic GPS positioning with Kalman filtering and smoothing: Theory and applications.” Ph.D. thesis, Dept. of Geodesy and Photogrammetry, Royal Institute of Technology, Stockholm, Sweden.
Julier, S. J., Uhlmann, J. K., and Durrant-Whyte, H. F. (1995). “A new approach for filtering nonlinear systems.” Proc., American Control Conf. 1628–1632.
Kennedy, S., Hamilton, J., and Martell, H. (2006). “Architecture and system performance of SPAN-NovAtel’s GPS/INS solution.” Proc., PLANS2006, IEEE/ION, 266–274.
Meditch, J. S. (1969). “Stochastic optimal linear estimation and control.” McGraw-Hill, New York.
Nassar, S. (2003). “Improving the inertial navigation system (INS) error model for INS and INS/DGPS applications.” Ph.D. thesis, Dept. of Geomatics Engineering, Univ. of Calgary, Calgary, Alberta, Canada.
Nassar, S., and Schwarz, K. P. (2001). “Bridging DGPS outages in kinematic applications using a simple algorithm for INS bias modeling.” Proc., Int. Symp. on Kinematic Systems in Geodesy, Geomatics and Navigation (KIS01), Canada.
Rauch, H. E. (1963). “Solutions to the linear smoothing problem.” IEEE Trans. Autom. Control, 8, 371–372.
Rauch, H. E., Tung, F., and Striebel, C. T. (1965). “Maximum likelihood estimates of linear dynamic systems.” AIAA J., 3(8), 1445–1450.
Schwarz, K. P., and Nassar, S. (2001). “A simple algorithm for bridging DGPS outages by INS bias modeling.” Proc., 3rd Int. Symp. on Mobile Mapping Technology.
Shin, E.-H. (2005). “Estimation techniques for low-cost inertial navigation.” Ph.D. thesis, Dept. of Geomatics Engineering, Univ. of Calgary, Calgary, Alberta, Canada.
Van der Merwe, R., and Wan, E. A. (2004). “Sigma-point Kalman filters for integrated navigation.” Proc., ION AM 04, 641–654.
Information & Authors
Information
Published In
Copyright
© 2007 ASCE.
History
Received: Dec 7, 2005
Accepted: Dec 8, 2006
Published online: Aug 1, 2007
Published in print: Aug 2007
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.