GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

ba755f3f-462c-45a2-a66f-4fdaeed758da20210526062943328wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON SYSTEMS AND CONTROL2224-28561991-876310.37394/23203http://wseas.org/wseas/cms.action?id=40731720211720211610.37394/23203.2021.16https://wseas.org/wseas/cms.action?id=23276GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman FilterReshmaVermaDepartment of Ece, M S Ramaiah Institute of Technology, IndiaLakshmiShrinivasanDepartment of Ece, M S Ramaiah Institute of Technology, IndiaKShreedarshanDepartment of Ece, M S Ramaiah Institute of Technology, IndiaNowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).52620215262021294301https://www.wseas.org/multimedia/journals/control/2021/a505103-009(2021).pdf10.37394/23203.2021.16.25https://www.wseas.org/multimedia/journals/control/2021/a505103-009(2021).pdf10.1109/icisce.2016.98Jingsen, Zheng, et al. "Integrating extreme learning machine with Kalman filter to bridge GPS outages." 2016 3rd International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2016. 10.1016/j.measurement.2017.01.053Yao, Yiqing, et al. "A hybrid fusion algorithm for GPS/INS integration during GPS outages." Measurement 103 (2017): 42- 51. 10.1109/taes.2002.1008998Qi, H., & Moore, J. B. (2002). Direct Kalman filtering approach for GPS/INS integration. IEEE Transactions on Aerospace and Electronic Systems, 38(2), 687-693. 10.1109/cis.2008.204Pise, Nitin Namdeo, and Parag Kulkarni. "A survey of semi-supervised learning methods." 2008 International Conference on Computational Intelligence and Security. Vol. 2. IEEE, 2008. Zhu, Xiaojin Jerry. Semi-supervised learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, 2005. 10.5772/intechopen.80600Kim, Youngjoo, and Hyochoong Bang. "Introduction to Kalman Filter and Its Applications." Introduction and Implementations of the Kalman Filter. IntechOpen, 2018. 10.1109/ivs.2009.5164318Ndjeng, Alexandre Ndjeng, et al. "Experimental comparison of kalman filters for vehicle localization." 2009 IEEE Intelligent Vehicles Symposium. IEEE, 2009. Sanjay H S, Bhargavi S, Thangadurai N, “Assessment of Psychophysical Variations in Human Beings with the Aid of Audiometry and Gap Detection Tests”, Journal of Engineering and Applied Sciences, Vol 12(23), 7351-57 (2017) 10.37394/232011.2020.15.2Lucjan Setlak, Rafal Kowalik, MEMS Electromechanical Microsystem as a Support System for the Position Determining Process with the Use of the Inertial Navigation System INS and Kalman Filter, WSEAS Transactions on Applied and Theoretical Mechanics, ISSN / E-ISSN: 1991-8747 / 2224-3429, Volume 14, 2019, Art. #11, pp. 105-117. 10.1109/eecs.2018.00009Shirin Yousefizadeh, Navid Vafamand, Jan Dimon Bendtsen, Mohammad Hassan Khooban, Frede Blaabjerg, Implementation of a Cubature Kalman Filter for Power Estimation of Non-ideal Constant Power Loads in a DC Microgrid, WSEAS Transactions on Power Systems, ISSN / EISSN: 1790-5060 / 2224-350X, Volume 14, 2019, Art. #15, pp. 122-129. 10.1109/med.2008.4602149N. Abdelkrim, N. Aouf, A. Tsourdos and B. White,”Robust nonlinear filtering for INS/GPS UAV localization” in proceedings of the Mediterranean Conference on Control and Automation (MED’08),pp.695-702,June 2008. 10.1007/s001900050236A.H. Mohamed and K.P. Schwarz , “ Adaptive Kalman filtering for INS/GPS” ,Journal of Geodesy,vol73,no.4,pp.193- 203,1999. 10.1109/iros.1994.407396Cooper, Simon, and Hugh DurrantWhyte. & quot; A Kalman filter model for GPSnavigation of land vehicles." Proceedings of IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS'94). Vol. 1. IEEE. Agarwal, Saurav, et al. & quot;GPS-INS Integration for Autonomous Navigation ofAircrafts.& quot; Proceedings of the International Conference on Navigation andCommunication (NavCom), Hyderabad, India. 2012.