Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-06-21T08:39:09.107Z Has data issue: false hasContentIssue false
  • English
  • Français

Airborne Earth Observation Positioning and Orientation by SINS/GPS Integration Using CD R-T-S Smoothing

Published online by Cambridge University Press:  20 September 2013

Xiaolin Gong  and
Tingting Qin
Xiaolin Gong*
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology and BeiHang University, School of Instrumentation Science and Opto-electronics Engineering; all Beijing, China)
Tingting Qin
Affiliation:
(Science and Technology on Inertial Laboratory, Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology and BeiHang University, School of Instrumentation Science and Opto-electronics Engineering; all Beijing, China)
*
(E-mail: gongxiaolin@buaa.edu.cn)
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper addresses the issue of state estimation in the integration of a Strapdown Inertial Navigation System (SINS) and Global Positioning System (GPS), which is used for airborne earth observation positioning and orientation. For a nonlinear system, especially with large initial attitude errors, the performance of linear estimation approaches will degrade. In this paper a nonlinear error model based on angle errors is built, and a nonlinear estimation algorithm called the Central Difference Rauch-Tung-Striebel (R-T-S) Smoother (CDRTSS) is utilized in SINS/GPS integration post-processing. In this algorithm, the measurements are first processed by the forward Central Difference Kalman filter (CDKF) and then a separate backward smoothing pass is used to obtain the improved solution. The performance of this algorithm is compared with a similar smoother based on an extended Kalman filter known as ERTSS through Monte Carlo simulations and flight tests with a loaded SINS/GPS integrated system. Furthermore, a digital camera was used to verify the precision of practical applications in a check field with numerous reference points. All these validity checks demonstrate that CDRTSS is a better method and the work of this paper will offer a new approach for SINS/GPS integration for Synthetic Aperture Radar (SAR) and other airborne earth observation tasks.

Keywords

SINS/GPS integrationNonlinear smoothingCD Kalman filterR-T-S Smoother
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 2 , March 2014 , pp. 211 - 225
Copyright
Copyright © The Royal Institute of Navigation 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Arasaratnam, I. and Haykin, S. (2009). Cubature Kalman filters. IEEE Transactions on Automatic Control, 54(6), 1254–1269.CrossRefGoogle Scholar
Brown, R.G. and Hwang, P.Y.C. (1992). Introduction to random signals and Applied Kalman Filtering. John Wiley and Sons. New York.Google Scholar
Crassidis, J.L. and Junkins, J.L. (2004). Optimal Estimation of Dynamic Systems. Chapman & Hall/CRC.Google Scholar
Crassidis, J.L. (2006). Sigma-point Kalman filtering for integrated GPS and inertial navigation. IEEE Transactions on Aerospace and Electronic Systems, 42(2), 750–756.CrossRefGoogle Scholar
Durbin, J. and Koopman, S.J. (2012). Time series analysis by state space methods, Oxford University Press.Google Scholar
Fang, J.C. and Gong, X.L. (2010). Predictive Iterated Kalman Filter for INS/GPS Integration and Its Application to SAR Motion Compensation. IEEE Transactions on Instrumentation and Measurement, 59(4), 909–915.Google Scholar
Farrell, J. and Barth, M. (1998) The Global Positioning System & Inertial Navigation, New York: McGraw-Hill.Google Scholar
Gelb, A. (1974). Applied Optimal Estimation. The Analytic Science Corporation.Google Scholar
Goshen-Meskin, D. and Bar-Itzhack, I.Y. (1992). Unified Approach to Inertial Navigation System Error Modeling. AIAA Journal of Guidance, Control and Dynamics, 15(3), 648–653.Google Scholar
Groves, P.D. (2008). Principles of GNSS, inertial, and multisensor integrated navigation systems, Artech House, London.Google Scholar
Hao, Y.L., Zhang, Z.Y. and Xia, Q.X. (2010). Research on Data Fusion for SINS/GPS Magnetometer Integrated Navigation based on Modified CDKF. Conference on Progress in Informatics and Computing (PIC), 1215–1219.Google Scholar
HT-A3 precision strapdown flexible gyroscope (1999). Shaan Xi Spaceflight the Great Wall Technology Co.Ltd. http://www.ec21.com/product-details/HT-A3-Precision-Strapdown-Flexible-Gyroscope-4691830.html. Accessed 31 July 2013.Google Scholar
Haykin, S. (2001). Kalman Filtering and Neural Networks. John Wiley & Sons, Inc., New York.CrossRefGoogle Scholar
Ito, K. and Xiong, K. (2000) Gaussian Filters for Nonlinear Filtering Problems. IEEE Transactions on Automatic Control, 45(5), 910–927.Google Scholar
Jht-i-b Quartz Accelerometers (1998). Shaan Xi Spaceflight the Great Wall Technology Co.Ltd. http://www.ec21.com/product-details/Jht-i-b-Quartz-Accelerometers-4686310.html. Accessed 30 July 2013.Google Scholar
Jwo, D.J. and Lai, S.Y. (2009). Navigation integration using the fuzzy strong tracking unscented Kalman Filter. The Journal of Navigation, 62, 303–322.CrossRefGoogle Scholar
Julier, S.J., Uhlmann, J.K. and Durrant-Whyte, H.F. (1995). A new approach for filtering nonlinear systems. Proceedings of the 1995 American Control Conference, 1628–1632.Google Scholar
Kennedy, S., Cosandier, D. and Hamilton, J. (2007). GPS/INS Integration in Real-Time and Post-processing with NovAtel's SPAN System. International Global Navigation Satellite Systems Society Sym-posium 2007, Sydney.Google Scholar
Kim, T.J. (2004). Motion Measurement for High-Accuracy Real-Time Airborne SAR. Proceedings of SPIE-The International Society for Optical Engineering, Radar Sensor Technology VIII and Passive Millimeter-Wave Imaging Technology VII, 5410, 36–44.CrossRefGoogle Scholar
Lee, J.K. and Jekeli, C. (2011). Rao-Blackwellized Unscented Particle Filter for a Handheld Unexploded Ordnance Geolocation System using IMU/GPS. The Journal of Navigation, 64, 327–340.Google Scholar
Rauch, H., Tung, F. and Striebel, C. (1965). Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3(8), 1445–1450.CrossRefGoogle Scholar
Sarkka, S. and Hartikainen, J. (2010). Sigma point methods in optimal smoothing of non-linear stochastic state space models. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 184–189.Google Scholar
Sarkka, S., Viikari, V., Huusko, M. and Jaakkola, K. (2012). Phase-Based UHF RFID Tracking with Non-Linear Kalman Filtering and Smoothing. IEEE Sensors Journal, 12(5), 904–910.Google Scholar
Simon, D. (2006). Optimal State Estimation: Kalman, H-Infinity, and Nonlinear Approaches. Wiley & Sons. First edition.Google Scholar
Toth, C.K. (2002). Sensor integration in airborne mapping. IEEE Transactions on Instrumentation and Measurement, 1(6), 1367–1373.Google Scholar
Van der Merwe, R., Wan, E.A., and Julier, S. (2004). Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation. Proceedings of the AIAA Guidance, Navigation & Control Conference (GNC), Providence, Rhode Island, 1735–1764.CrossRefGoogle Scholar
Wang, Y.F., Sun, F.C., Zhang, Y.A. and Min, H.B. (2012). Central Difference Particle Filter Applied to Transfer Alignment for SINS on Missiles. IEEE Transactions on Aerospace and Electronic Systems, 48(1), 375–387.Google Scholar
Wu, Y., Hu, D., Wu, M., and Hu, X. (2006). A numerical integration perspective on Gaussian filters. IEEE Transactions on Signal Processing, 54(8), 2910–2921.Google Scholar
Xu, Z., Li, Y., Rizos, C. and Xu, X. (2010). Novel hybrid of LS-SVM and Kalman filter for GPS/INS integration. The Journal of Navigation, 63, 289–299.Google Scholar