Comparative Analysis of Fusion Algorithms in a Loosely-Coupled Integrated Navigation System on the Basis of Real Data Processing
https://doi.org/10.17285/0869-7035.0001
Abstract
The paper presents a comparative analysis of the extended Kalman filter (EKF) and the sigma-point Kalman filter (SPKF) applied to solve the problem of SINS/GNSS integration based on a loosely-coupled integration scheme. Complete stochastic measurement models of MEMS inertial sensors are considered. The efficiency of the EKF and the SPKF is evaluated using real experimental data on complex motion from an SINS based on MEMS technology and a GNSS receiver with a double antenna. The estimation accuracy of navigation parameters using the EKF and the SPKF in the presence of the GNSS signal and during the GNSS outages is analyzed. The results of the statistical analysis of the errors in estimating navigation parameters for different periods of GNSS signal outage are considered.
About the Authors
N. Al Bitar
Bauman Moscow State Technical University, Moscow, Russia
Russian Federation
Russian Federation
A. I. Gavrilov
Bauman Moscow State Technical University, Moscow, Russia
Russian Federation
Russian Federation
References
1. Salychev, O.S., Inertial Systems in Navigation and Geophysics, Moscow: Bauman MSTU Press, 1998.
2. Grewal, M.S., Weill, L.R., and Andrews, A.P., Global Positioning Systems, Inertial Navigation, and Integration, 2nd ed., New York: John Wiley & Sons, 2007.
3. Matveev, V.V. and Raspopov, V.Ya., Osnovy postroeniya besplatformennykh inertsial’nykh navigatsionnykh sistem (Fundamentals of Designing Strapdown Inertial Navigation Systems), St. Petersburg: TsNII Elektropribor, 2009.
4. Crassidis, J.L. and Junkins, J.L., Optimal Estimation of Dynamic Systems, 2nd ed., New York: CRC Press, 2011.
5. Kong, X., Wu, W., Zhang, L., and Wang, Y., Tightly-coupled stereo visual-inertial navigation using point and line features, Sensors, 2015, 15(6), pp. 12816–12833.
6. Shang, J., Hu, X., Gu, F., Wang, D., and Yu, S., Improvement schemes for indoor mobile location estimation: A survey, Mathematical Problems in Engineering, 2015.
7. Wan, E.A. and Van Der Merwe, R., The unscented Kalman filter for nonlinear estimation, Adaptive Systems for Signal Processing, Communications, and Control Symposium, IEEE, 2000, pp. 153–158.
8. Li, F., Chang, L., Hu, B., and Li, K., Marginalized unscented quaternion estimator for integrated INS/GPS, The Journal of Navigation, 2016, 69(5), pp. 1125–1142.
9. LaViola, J.J., A comparison of unscented and extended Kalman filtering for estimating quaternion motion, American Control Conference, 2003, vol. 3. pp. 2435–2440.
10. El-Sheimy, N., Shin, E.H., and Niu, X., Kalman filter face-off: Extended vs. unscented Kalman filters for integrated GPS and MEMS inertial, Inside GNSS, 2006, vol. 1, no. 2, pp. 48–54.
11. Crassidis, J.L., Sigma-point Kalman filtering for integrated GPS and inertial navigation, IEEE Transactions on Aerospace and Electronic Systems, 2006, vol. 42, no. 2, pp. 750–756.
12. Konakov, A.S., Shavrin, V.V., Tislenko, V.I., and Savin, A.A., Comparative analysis of the root-mean-square error in determining an object’s coordinates of in a strapdown inertial navigation system using various nonlinear filtering algorithms, Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2012, no. 1-1 (25), pp. 5–9.
13. Shavrin, V.V., Tislenko, V.I., Lebedev V.Yu., Konakov A.S., Filimonov, V.A., and Kravets, A.P., Quasi-optimal estimation of GNSS signal parameters in coherent reception mode using the sigma-point Kalman filter algorithm, Giroskopiya i Navigatsiya, 2016, no. 3 (94), pp. 26–37.
14. Bolotin, Yu.V. and Fatekhrad, M., Pedestrian navigation using a strapdown inertial navigation system (SINS) on a foot, Rossiiskii zhurnal biomekhaniki, vol. 19, no. 1, 2015, pp. 25–36.
15. Ryu, J.H., Gankhuyag, G., and Chong, K.T., Navigation system heading and position accuracy improvement through GPS and INS data fusion, Journal of Sensors, 2016, vol. 2016, pp. 1–6.
16. Jekeli, C., Inertial Navigation Systems with Geodetic Applications, Berlin: Walter de Gruyter, 2001.
17. Emel’yantsev, G.I. and Stepanov, A.P., Integrirovannye inertsial’no-sputnikovye sistemy orientatsii i navigatsii (Integrated INS-GNSS Orientation and Navigation Systems), Peshekhonov, V.G., Ed., St. Petersburg: Kontsern TsNII Elektropribor, 2016.
18. Matveev, V.V., Inertsial'nye navigatsionnye sistemy: Uchebnoye posobiye (Inertial Navigation Systems: Tutorial), Tula: TulGU, 2012.
19. Quinchia, A.G., Falco, G., Falletti, E., Dovis, F., and Ferrer, C., A comparison between different error modeling of MEMS applied to GPS/INS integrated systems, Sensors, 2013, vol. 13, no. 8, pp. 9549–9588.
20. Martin, H., Groves, P., and Newman, M., The limits of in‐run calibration of MEMS inertial sensors and sensor arrays, Navigation: Journal of The Institute of Navigation, 201663(2), pp. 127–143.
21. Hou, H. and El-Sheimy, N., Inertial sensors errors modeling using Allan variance, Proceedings of the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS/GNSS 2003), 2001, pp. 2860–2867.
22. SBG Systems, Ekinox INS – User Manual: EKINOXINSUM.1.2 Revision 1.2, Mar 6, 2014.
23. Gonzalez, R., Catania, C.A., Dabove, P., Taffernaberry, J.C., and Piras, M., Model validation of an open-source framework for post-processing INS/GNSS systems, Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), Porto, 2017, pp. 201–208.
24. Gonzalez, R., NaveGo: an open-source MATLAB/GNU Octave toolbox for simulating integrated navigation systems and performing Allan variance analysis, 2016. URL: www.github.com/rodralez/NaveGo/.
25. NovAtel Inc. Inertial Explorer R User Guide version 8.50, OM-20000106, Rev 9. Canada, 2013.
26. Hong, S., Lee, M.H., Chun, H.H., Kwon, S.H., and Speyer, J.L., Observability of error states in GPS/INS integration, IEEE Transactions on Vehicular Technology, 2005, vol. 54, no. 2, pp. 731–743.
27. Tang, Y., Wu, Y., Wu, M., Wu, W., Hu, X., and Shen, L., INS/GPS integration: Global observability analysis, IEEE Transactions on Vehicular Technology, 2009, vol. 58, no. 3, pp. 1129–1142.
28. Klein, I. and Diamant, R., Observability Analysis of Heading Aided INS for a Maneuvering AUV, Navigation: Journal of The Institute of Navigation, 2018, 65(1), pp. 73–82.
29. Shen, K., Xia, Y., Wang, M., Neusypin, K.A., and Proletarsky, A.V., Quantifying Observability and Analysis in Integrated Navigation, Navigation: Journal of The Institute of Navigation, 2018, 65(2), 169–181.
30. Al Bitar, N. and Gavrilov, A.I., Intelligent computing technologies in roblems of improving the accuracy of integrated navigation systems, Vestnik MGTU im. N.E. Baumana, Ser. Priborostroenie, 2019, no.1, pp. 62–89.
Review
For citations:
Al Bitar N., Gavrilov A.I. Comparative Analysis of Fusion Algorithms in a Loosely-Coupled Integrated Navigation System on the Basis of Real Data Processing. Giroskopiya i Navigatsiya. 2019;27(3):31-52. (In Russ.) https://doi.org/10.17285/0869-7035.0001
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