Yunki Kim, Jaehyun Park, Jangmyung Lee
(Dept. of Electrical Engineering, Pusan National University, Pusan 609-735, Korea)
Abstract：This paper proposes a technique that global positioning system (GPS) combines inertial navigation system (INS) by using unscented particle filter (UPF) to estimate the exact outdoor position. This system can make up for the weak point on position estimation by the merits of GPS and INS. In general, extended Kalman filter (EKF) has been widely used in order to combine GPS with INS. However, UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear, irregular distribution errors. In this paper, the accuracy of UPF is proved through the simulation experiment, using the virtual-data needed for the test.
Key words：global positioning system (GPS); unscented particle filter (UPF); navigation; inertial navigation system (INS); strapdown inertial navigation system (SDINS)
CLD number： TN967 Document code： A
Article ID： 1674-8042(2013)01-0047-05 doi： 10.3969/j.issn.1674-8042.2013.01.011
References
[1] Davidson P, Hautamki J, Collin J. Using low-cost MEMS 3D accelerometer and one gyro to assist GPS based car navigation system. [2012-08-22]. http://www.tkt.cs.tut.fi/research/nappo.htm.
[2] Park J Y, Lee J H, Nam D K, et al. Investigations on GPS/INS integration for land vehicle navigation. In: Proceedings of KIIS fall conference, 2009, 19(2): 3-360.
[3] Lee J K. The estimation methods for an integrated INS/GPS UXO geolocation system. The Ohio State University Report, No. 493, 2009.
[4] Aggarwal P, Syed Z, Noureldin A, et al. MEMS-based integrated navigation. Artech House, 2010.
[5] Aggarwal P, Syed Z, El-sheimy N. Hybrid extended particle filter(HEPF) for integrated inertial navigation and global positioning system. Measurement Science and Technology, 2009, 20(5): 1-9.
[6] Woodman O J. An introduction to inertial navigation. University of Cambridge Technical Report, No.696, 2007.
[7] Siouris G M. Aerospace avionics systems a modern synthesis. Academic Press Inc., 1993: 67.
[8] Skog I, Handel P. Time synchronization errors in GPS-aided inertial navigation system. IEEE Transactions on Intelligent Transportation Systems, 2011.
[9] Hwang S Y, Lee and J M. Estimation of attitude and position of moving objects using multi-filtered inertial navigation system. The Transactions of KIEE, 2011, 60(12): 2183-2396.
[10] Farrell J A. Aided navigation. McGrawHill, 2008.
[11] de Melo L F, Mangili J F Jr. Trajectory planning for nonholonomic mobile robot using extended Kalman filter. Mathematical Problems in Engineering, 2010: 1-22.
[12] Wan E A, van der Merwe R. The unscented Kalman filter for nonlinear estimation. In: Proceedings of IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium. IEEE, 2000: 153-158.
[13] Hartikainen J, Sarkka S. Optimal filtering with Kalman filters and smoothers-a manual for matlab toolbox EKF / UKF. Biomedical Engineering, 2008: 1-57.
[14] Haykin S, Kalman filtering and neural networks, John Wiley & Sons, New York, 2001.
[15] Gustafsson F, Gunnarsson F, Bergman N, et al. Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 425-437.
[16] YANG Ning, TIAN Wei-Feng, JIN Zhi-hua, et al. Particle filter for sensor fusion in a land vehicle navigation system. Measurement Science and Technology, 2005, 16: 677-681.
[17] CHEN Zhe. Bayesian filtering: from Kalman filters to particld filters, and beyound. Citeseer, 2003: 1-69.
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