Gaussian Process for GPS Receiver Predictor and INS GPS Integration
Engineering and Technology Journal,
2023, Volume 41, Issue 2, Pages 1-13
AbstractGlobal Positioning System (GPS) has become important and necessary in daily life. It is possible to reach any destination using GPS, which is included in many lands and marine applications. In this work, GPS was applied to a real navigation boat, integrated with the inertial navigation system (INS) device, and installed on the boat. The navigational devices were linked to the (mission planer) program, through which the results of the navigation process were shown. The system can provide better navigation performance accuracy and reliability due to the integration between GPS and INS. The data extracted from the navigation devices are processed using the Gaussian process (GP) prediction algorithm, to perform the GPS synchronization with the INS and predict the GPS cut-off values for specified periods. The prediction results of the GP algorithm are effective for the cut-off GPS data as the apparent error amount of the algorithm is low. In addition the inertial navigation system provides the location, speed, and position of the boat, but it contains a cumulative error that increases over time. On the other hand, the GPS better accuracy with a lower data rate than the INS, so the integration system between INS/GPS is necessary. It must be developed to overcome the negatives in both systems. Two types of integration were introduced and implemented herein: loosely and tightly. From the results obtained, one can see that the tight system is better at improving errors.
- The GPS data values were predicted when a signal was cut off for any reason using the Gaussian Process algorithm.
- Synchronization between GPS values and INS values for integration was employed.
- Implementation of the work was done by the program MATLAB.
- Tthe integration was carried out using the Extended Kalman filter.
 B. Rahmatullah, A. A. Zaidan, F. Mohamed, and A. Sali, Multi-complex attributes analysis for optimum GPS baseband receiver tracking channels selection, In 2017 4th international conference on control, decision and information technologies (CoDIT) (pp. 1084-1088). IEEE.,( 2017). doi : 10.1109/CoDIT.2017.8102743
 M. Advani, and D. S. Weile, Position and orientation inference via on-board triangulation, Plos one., 12 (2017) e0180089. doi.org/10.1371/journal.pone.0180089
 R., Sabatini, T., Moore, and S. Ramasamy, Global navigation satellite systems performance analysis and augmentation strategies in aviation, Progress in Aerospace Sciences., 95 (2017) 45-98. doi.org/10.1016/j.paerosci.2017.10.002
 D. Wang, X. Xu, and Y. Zhu, A novel hybrid of a fading filter and an extreme learning machine for GPS/INS during GPS outages, Sensors., 18 (2018) 3863. doi.org/10.3390/s18113863
 L. Chen, and F. Jiancheng, A hybrid prediction method for bridging GPS outages in high-precision POS application. IEEE Transactions on Instrumentation and Measurement., 63 (2014) 1656-1665. https://doi.org/10.1109/TIM.2013.2292277
 G. T. Schmidt, and R. E. Phillips, INS/GPS Integration Architectures, Massachusetts Inst of Tech, Lexington, MA. RTO-EN-SET-116., (2010). https://apps.dtic.mil/sti/pdfs/ADA581020.pdf
 Y. Zhang, C. Shen, J. Tang, and J. Liu, Hybrid algorithm based on MDF-CKF and RF for GPS/INS system during GPS outages, IEEE Access., 6 (2018) 35343-35354. https://doi.org/10.1109/ACCESS.2018.2849217
 Y. Tang, Y. Wu, M. Wu, W. Wu, X. Hu, and L. Shen, INS/GPS integration: Global observability analysis, IEEE Transactions on Vehicular Technology., 58 (2008) 1129-1142. https://doi.org/10.1109/TVT.2008.926213
 A. Noureldin, A. El-Shafie, and M. Bayoumi, "GPS/INS integration utilizing dynamic neural networks for vehicular navigation," Information fusion., 12(2011) 48-57. https://doi.org/10.1016/j.inffus.2010.01.003
 C. H. Tseng, S. F. Lin, D.J. Jwo, Fuzzy adaptive cubature Kalman filter for integrated navigation systems, Sensors., 16 (2016) 1167. https://doi.org/10.3390/s16081167
 X. C. Tian, and C. D. Xu, Novel hybrid of strong tracking Kalman filter and improved radial basis function neural network for GPS/INS integrated navigation, In 2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE), IEEE., (2016) 72-76. https://doi.org/10.1109/CCSSE.2016.7784356
 Y. Yao, X. Xu, C. Zhu, and C. Y. Chan, A hybrid fusion algorithm for GPS/INS integration during GPS outages, Measurement., 103 (2017) 42-51. https://doi.org/10.1016/j.measurement.2017.01.053
 X. Chen, C. Shen, W. B. Zhang, M. Tomizuka, Y. Xu, and K. Chiu, Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages, Measurement., 46 (2013) 3847-3854. https://doi.org/10.1016/j.measurement.2013.07.016
 N. Q. Vinh, INS/GPS integration system using street return algorithm and compass sensor, Procedia Computer Science., 103 (2017) 475-482. https://doi.org/10.1016/j.procs.2017.01.030
 A. Schumacher, Integration of a gps aided strapdown inertial navigation system for land vehicles, Master of Science Thesis, KTH Electrical Engineering., (2006).
 C. K. Arthur, V. A. Temeng, and Y. Y. Ziggah, Novel approach to predicting blast-induced ground vibration using Gaussian process regression, Engineering with Computers., 36 (2020) 29-42. https://doi.org/10.1007/s00366-018-0686-3
 H. S. Jo, C. Park, E. Lee, H. K. Choi, and J. Park, Path loss prediction based on machine learning techniques: Principal component analysis, artificial neural network, and Gaussian process, Sensors., 20 (2020) 1927. https://doi.org/10.3390/s20071927
 R. Senanayake, S. O'Callaghan, and F. Ramos, Predicting spatio-temporal propagation of seasonal influenza using variational Gaussian process regression, In Proceedings of the AAAI Conference on Artificial Intelligence., 30 (2016). https://doi.org/10.1609/aaai.v30i1.9899
 D. M. Bui, H. Q. Nguyen, Y. Yoon, S. Jun, M. B. Amin, and S. Lee, Gaussian process for predicting CPU utilization and its application to energy efficiency, Applied Intelligence., 43 (2015) 874-891. https://doi.org/10.1007/s10489-015-0688-4
 Titsias, K. Michalis and N. D. Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. Adv. in Neural Inf. Proc. Sys., (2004). https://proceedings.neurips.cc/paper/2003/file/9657c1fffd38824e5ab0472e022e577e-Paper.pdf
 H. Tolba, N. Dkhili, J. Nou, J. Eynard, S. Thil, and S. Grieu, GHI forecasting using Gaussian process regression, In IFAC Workshop on Control of Smart Grid and Renewable Energy Systems., (2019). https://hal.archives-ouvertes.fr/hal-02051993
 W. Wang, Z. Y. Liu, and R. R. Xie, Quadratic extended Kalman filter approach for GPS/INS integration, Aerospace science and technology., 10 (2006) 709-713. https://doi.org/10.1016/j.ast.2006.03.003
 N. Q. Vinh, INS/GPS integration system using street return algorithm and compass sensor, Procedia Computer Science, 103 (2017) 475-482. https://doi.org/10.1016/j.procs.2017.01.030
 A. Noureldin, T. B. Karamat, M. D. Eberts, and A. El-Shafie, Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Transactions on vehicular technology., 58 (2008) 1077-1096. doi: 10.1109/TVT.2008.926076
 M. St-Pierre, and D. Gingras, Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system, In IEEE Intelligent Vehicles Symposium IEEE., (2004) 831-835. doi: 10.1109/IVS.2004.1336492
 Öztürk, A. (2003). Development, implementation, and testing of a tightly coupled integrated INS/GPS system (Doctoral dissertation, METU). https://etd.lib.metu.edu.tr/upload/4/1093087/index.pdf
 M. Wang, W. Wu, P. Zhou, and X. He, State transformation extended Kalman filter for GPS/SINS tightly coupled integration, Gps Solutions., 22 (2018) 1-12. https://doi.org/10.1007/s10291-018-0773-3
 J. Zhou, S. Knedlik, and O. Loffeld, INS/GPS tightly-coupled integration using adaptive unscented particle filter, The Journal of Navigation., 63 (2010) 491-511. https://doi.org/10.1017/S0373463310000068
 Y. Li, J. Wang, and C. Rizos, Comparison of the extended and sigma-point kalman filters on inertial sensor bias estimation through tight integration of GPS and INS, In Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation ION GNSS., (2006) 1625-1634.
 T. Chai, and R. R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE), Geoscientific Model Development Discussions., 7 (2014) 1525-1534. https://doi.org/10.5194/gmd-7-1247-2014
 H. N. AbdulRihda, F. M. Mohammed, S. A. Aziez, Integration of (INS/DGPS ) System For Airplane Landing Phase, Msc. Thesis, university of technology, Iraq., (2015). https://ieeeauthorcenter.ieee.org/wp-content/uploads/IEEE-Reference-Guide.pdf
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