Artificial intelligence based methods for accuracy improvement of integrated navigation systems during gnss signal outages: an analytical overview
https://doi.org/10.17285/0869-7035.0014
Abstract
The limitations of Kalman filter (KF) have motivated researchers to consider alternative methods of integrating inertial navigation systems (INS) and global navigation satellite systems (GNSS), predominantly based on artificial intelligence (AI). Over the past two decades, a great number of research gained in order to validate the possibility of using AI methods in the field of integrated navigation systems. Different approaches have been proposed for combining AI modules with the other parts of the INS/GNSS system. The article suggests a new classification of the resulting schemes based on the functionality of AI modules in the INS/GNSS system. The paper also provides a brief explanation of each scheme with its advantages and disadvantages. Some aspects that need to be considered in future research in this field are also highlighted.
About the Authors
N. Al Bitar
Department of Automatic Control Systems, Bauman Moscow State Technical University, Moscow, Russia
Russian Federation
Russian Federation
A. I. Gavrilov
Department of Automatic Control Systems, Bauman Moscow State Technical University, Moscow, Russia
Russian Federation
Russian Federation
W. Khalaf
Department of Electronic & Mechanical Systems, Higher Institute for Applied Sciences and Technology, Damascus, Syria
Syrian Arab Republic
Syrian Arab Republic
References
1. Grewal, M.S., Weill, L.R., & Andrews, A.P., Global positioning systems, inertial navigation, and integration, John Wiley & Sons, 2007.
2. Farrell, J., & Barth, M., The global positioning system and inertial navigation, New York, Mcgrawhill, 1999, vol. 61.
3. Chiang, K. W., Noureldin, A., & El-Sheimy, N., Multisensor integration using neuron computing for land-vehicle navigation. GNSS solutions, 2003, 6(4), 209–218.
4. Salychev, O.S., Inertial systems in navigation and geophysics, Moscow, Russia, Bauman MSTU Press, 1998, pp. 11–98.
5. Jekeli, C., Inertial navigation systems with geodetic applications, Walter de Gruyter, 2012.
6. Vörsmann, P., Kaschwich, C., Krüger, T., Schnetter, P., & Wilkens, C.S., MEMS based integrated navigation systems for adaptive flight control of unmanned aircraft – State of the art and future developments, Gyroscopy and Navigation, 2012, 3(4), 235–244.
7. Schwarz, K. P., El-Sheimy, N., & Liu, Z., Fixing GNSS cycle slips by INS/GNSS–methods and experiences, Proc. KIS94, 1994, 265–275.
8. Angrisano, A., GNSS/INS integration methods, Dottorato di ricerca (PhD) in Scienze Geodetiche e Topografiche Thesis, Universita’degli Studi di Napoli PARTHENOPE, Naple, 21, 2010.
9. Crassidis, J.L., & Junkins, J.L., Optimal estimation of dynamic systems, Chapman and Hall/CRC, 2004.
10. Julier, S.J., & Uhlmann, J.K., Unscented filtering and nonlinear estimation, Proceedings of the IEEE, 2004, 92(3), 401–422.
11. Vanicek, P., & Omerbasic, M., Does a navigation algorithm have to use a Kalman filter? Canadian aeronautics and space journal, 1999, 45(3), 292–296,
12. Hong, S., Lee, M.H., Chun, H.H., Kwon, S.H., & Speyer, J.L., Observability of error states in GNSS/INS integration, IEEE Transactions on Vehicular Technology, 2005, 54(2), 731–743.
13. Tang, Y., Wu, Y., Wu, M., Wu, W., Hu, X., & Shen, L., INS/GNSS integration: Global observability analysis, IEEE Transactions on Vehicular Technology, 2008, 58(3), 1129–1142.
14. Cawsey, A., The essence of artificial intelligence, Prentice Hall PTR, 1997.
15. Symon, H., Neural networks: a comprehensive foundation, Prentice-Hall, 1999.
16. Jang, J.S., ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 1993, 23(3), 665–685.
17. Eiben, A.E., & Smith, J.E., Introduction to evolutionary computing, Berlin, Springer, vol. 53, p. 18. 2003.
18. Giarratano, J. C., & Riley, G., Expert systems: Principles and programming, Brooks/Cole Publishing Co, 1989.
19. Mitchell, M., An introduction to genetic algorithms, MIT Press, 1998.
20. Uhr, L., & Honavar, V. (Eds.), Artificial intelligence and neural networks: steps toward principled integration, Academic Press, 1994.
21. Wang, X., Li, K., Gao, P., & Wang, W., Reinforced ultra-tightly coupled GNSS/INS system for challenging environment, Mathematical Problems in Engineering, 2014.
22. Refan, M. H., Dameshghi, A., & Kamarzarrin, M., Utilizing hybrid recurrent neural network and genetic algorithm for predicting the pseudo-range correction factors to improve the accuracy of RTDGNSS, Gyroscopy and Navigation, 2015, 6(3), 197–206.
23. El-Sheimy, N., Chiang, K. W., & Noureldin, A., The utilization of artificial neural networks for multisensor system integration in navigation and positioning instruments, IEEE Transactions on instrumentation and measurement, 2006, 55(5), 1606–1615.
24. Noureldin, A., Osman, A., & El-Sheimy, N., A neuro-wavelet method for multi-sensor system integration for vehicular navigation, Measurement science and technology, 2003, 15(2), 404.
25. Noureldin, A., El-Shafie, A., & El-Sheimy, N., Adaptive neuro-fuzzy module for inertial navigation system/global positioning system integration utilising position and velocity updates with real-time cross-validation, IET Radar, Sonar & Navigation, 2007, 1(5), 388–396.
26. Chiang, K. W., Noureldin, A., & El-Sheimy, N., Constructive neural-networks-based MEMS/GNSS integration scheme, IEEE transactions on aerospace and electronic systems, 2008, 44(2), 582–594.
27. Fahlman, S. E., & Lebiere, C., The cascade-correlation learning architecture, Advances in neural information processing systems, 1990, pp. 524–532.
28. Adusumilli, S., Bhatt, D., Wang, H., Bhattacharya, P., & Devabhaktuni, V., A low-cost INS/GNSS integration methodology based on random forest regression, Expert Systems with Applications, 2013, 40(11), 4653–4659.
29. Breiman, L., Random forests. Machine learning, 2001, 45(1), 5–32.
30. Moré, J.J., The Levenberg-Marquardt algorithm: implementation and theory, Numerical analysis, Springer, Berlin, Heidelberg, 1978, pp. 105–116.
31. Sharaf, R., Noureldin, A., Osman, A., & El-Sheimy, N., Online INS/GNSS integration with a radial basis function neural network, IEEE Aerospace and Electronic Systems Magazine, 2005, 20(3), 8–14.
32. Sharaf, R., & Noureldin, A., Sensor integration for satellite-based vehicular navigation using neural networks, IEEE transactions on neural networks, 2007, 18(2), 589–594.
33. Taha, M.R., Noureldin, A., & El-Sheimy, N., Improving INS/GNSS positioning accuracy during GNSS outages using fuzzy logic, Proceedings of the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS/GNSS 2003), 2001, pp. 499–508.
34. Bezdek, J.C., Objective function clustering, Pattern recognition with fuzzy objective function algorithms, Springer, Boston, MA, 1981, pp. 43–93.
35. Noureldin, A., El-Shafie, A., & Taha, M. R., Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation, Engineering Applications of Artificial Intelligence, 2007, 20(1), 49–61.
36. Hasan, A.M., Samsudin, K., Ramli, A.R., & Azmir, R.S., Automatic estimation of inertial navigation system errors for global positioning system outage recovery, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2011, 225(1), 86–96.
37. Hasan, A.M., Samsudin, K., & Ramli, A.R., Optimizing of ANFIS for estimating INS error during GNSS outages, Journal of the Chinese Institute of Engineers, 2011, 34(7), 967–982.
38. Kennedy, J., Particle swarm optimization. Encyclopedia of machine learning, 2010, 760–766.
39. Hou, H., & 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 GNSS/GNSS 2003), 2001, pp. 2860–2867.
40. Goodall, C., Syed, Z., & El-Sheimy, N., Improving INS/GNSS navigation accuracy through compensation of Kalman filter errors, IEEE Vehicular Technology Conference, 2006, pp. 1–5.
41. Abdel-Hamid, W., Noureldin, A., & El-Sheimy, N., Adaptive fuzzy prediction of low-cost inertial-based positioning errors, IEEE Transactions on Fuzzy Systems, 2007, 15(3), 519–529.
42. Noureldin, A., Karamat, T.B., Eberts, M.D., & El-Shafie, A., Performance enhancement of MEMS-based INS/GNSS integration for low-cost navigation applications, IEEE Transactions on vehicular technology, 2009, 58(3), 1077–1096.
43. Xu, Z., Li, Y., Rizos, C., & Xu, X., Novel hybrid of LS-SVM and Kalman filter for GNSS/INS integration, The Journal of Navigation, 2010, 63(2), 289–299.
44. Vapnik, V., The support vector method of function estimation. In Nonlinear Modeling, Springer, Boston, MA, 1998, pp. 55–85.
45. Chen, X., Shen, C., Zhang, W. B., Tomizuka, M., Xu, Y., & Chiu, K., Novel hybrid of strong tracking Kalman filter and wavelet neural network for GNSS/INS during GNSS outages, Measurement, 2013, 46(10), 3847–3854.
46. Boutayeb, M., & Aubry, D., A strong tracking extended Kalman observer for nonlinear discrete-time systems, IEEE Transactions on Automatic Control, 1999, 44(8), 1550–1556.
47. Zhang, J., Walter, G. G., Miao, Y., & Lee, W. N. W., Wavelet neural networks for function learning, IEEE transactions on Signal Processing, 1995, 43(6), 1485–1497.
48. Wang, X., Chen, J. X., & Ni, W., A hybrid prediction method and its application in the distributed low-cost INS/GNSS integrated navigation system, IEEE 18th International Conference on Information Fusion (Fusion), 2015, pp. 1205–1212.
49. Wang, J.J., Wang, J., Sinclair, D., & Watts, L., Neural network aided Kalman filtering for integrated GNSS/INS geo-referencing platform, Proc. 5th Int. Symp. Mobile Mapping Technol, 2007, pp. 1–6.
50. Chen, L., & Fang, J., A hybrid prediction method for bridging GNSS outages in high-precision POS application, IEEE Transactions on Instrumentation and Measurement, 2014, 63(6), 1656–1665.
51. Jingsen, Z., Wenjie, Z., Bo, H., & Yali, W., Integrating Extreme Learning Machine with Kalman Filter to Bridge GNSS Outages, IEEE 3rd International Conference on Information Science and Control Engineering (ICISCE), 2016, pp. 420–424.
52. Huang, G. B., Wang, D. H., & Lan, Y., Extreme learning machines: a survey, International journal of machine learning and cybernetics, 2011, 2(2), 107–122.
53. Yao, Y., Xu, X., Zhu, C., & Chan, C.Y., A hybrid fusion algorithm for GNSS/INS integration during GNSS outages, Measurement, 2017, 103, 42–51.
54. Yao, Y., & Xu, X., A RLS-SVM aided fusion methodology for INS during GNSS outages, Sensors, 2017, 17(3), 432.
Review
For citations:
Al Bitar N., Gavrilov A.I., Khalaf W. Artificial intelligence based methods for accuracy improvement of integrated navigation systems during gnss signal outages: an analytical overview. Giroskopiya i Navigatsiya. 2019;27(4):3-28. (In Russ.) https://doi.org/10.17285/0869-7035.0014
Views:
1
Email this article 