Hybrid Positioning Technique Based Integration of GPS/INS for an Autonomous Vehicle Navigation

A.N Ouda; Amr Mohamed

doi:10.3849/aimt.01498

Authors

A.N Ouda University of Ontario Institute of Technology
Amr Mohamed University of Ontario Institute of Technology

DOI:

https://doi.org/10.3849/aimt.01498

Keywords:

autonomous vehicles, GPS/INS navigation systems, hybrid positioning technique, Kalman filter, sensor fusion

Abstract

This paper presents a hybrid positioning technique combining both loosely and tightly coupled Kalman Filter (KF) algorithms for an autonomous multi-wheeled combat vehicle. The developed algorithm is able to provide accurate positioning information even if number of visible satellites falls below the minimum due to the harsh operation environments. Two modes of operation were considered which automatically switch between them according to the number of visible satellites in order to correct the INS drift. Furthermore, a performance comparison between fifteen and eighteen KFs states is conducted. A simulation of the developed algorithm is performed, using a SATNAV navigation toolbox and the collected data from real sensors mounted on a ground vehicle. The experimental results validated effectiveness of the developed algorithm.

Author Biographies

A.N Ouda, University of Ontario Institute of Technology

Dr.Ahmed Ouda is an Associate Professor at the Military Technical Research Centre (TRC) in Cairo. Currently, he is a visiting scholar, University of Ontario Institute of Technology (UOIT). He obtained his bachelor, master degree from Military Technical College, Egypt. He received his PhD degree in “Performance Investigation of adaptive guidance algorithms and its effectiveness” from the Military Technical College, Egypt. Dr. Ouda has contributed to several typical research projects in the field of missiles control systems.
Amr Mohamed, University of Ontario Institute of Technology

Amr Mohamed is an assistant professor at Military Technical college (MTC), Egypt. He is currently a visiting scholar, at the Faculty of Engineering and Applied Science of the University of Ontario Institute Of Technology. He obtained his Bachelor and Master’s degree from Military Technical College, Egypt. obtained his PhD degree from University of Ontario Institute of Technology. He has contributed to several typical research projects in the field of multi-wheeled combat vehicles. Furthermore, he is concerned about vehicles control systems issues comprising modelling, simulation and control.

References

BROWN, M., J. FUNKE, S. ERLIEN and J.C. GERDES. Safe Driving Envelopes for Path Tracking in Autonomous Vehicles. Control Engineering Practice, 2017, 61, pp. 307-316. ISSN 0967-0661.

MOHAMED, A., M. EL-GINDY, J. REN and H. LANG. Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle. In: ASME In-ternational Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Cleveland: ASME, 2017, V003T01A002. DOI 10.1115/DETC2017-67025.

JARVIS, R. An All-Terrain Intelligent Autonomous Vehicle with Sensor-Fusion-Based Navigation Capabilities. Control Engineering Practice, 1996, 4(4), pp. 481-486. DOI 10.1016/0967-0661(96)00029-9.

MOHAMED A, J. REN, H. LANG, M. EL-GINDY. Optimal Collision Free Path Planning for an Autonomous Articulated Vehicle with Two Trailers. In: IEEE In-ternational Conference on Industrial Technology (ICIT). Toronto: IEEE, 2017, pp. 860-865. DOI 10.1109/ICIT.2017.7915472.

MOHAMED, A., A.N. OUDA, J. REN and M. EL-GINDY. Processor-in-the-Loop Co-Simulations and Control System Design for a Scaled Autonomous Multi-Wheeled Combat Vehicle. International Journal of Automation and Control, 2020, 14(2), pp. 138-160. DOI 10.1504/IJAAC.2020.105516.

WU, Z., M. YAO, H. MA and W. JIA. Improving Accuracy of the Vehicle Attitude Estimation for Low-Cost INS/GPS Integration Aided by the GPS-Measured Course Angle. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2), pp. 553-564. DOI 10.1109/TITS.2012.2224343.

SANTOS, M.C.P., L.V. SANTANA, M. SARCINELLI-FILHO and R. DCARELLIE. Indoor Low-Cost Localization System for Controlling Aerial Ro-bots. Control Engineering Practice, 2017, 61, pp. 93-111. DOI 10.1016/j.conengprac.2017.01.011.

SHAGHAGHIAN, A. and P. KARIMAGHAEE. Improving GPS/INS Integration Using FIKF‐Filtered Innovation Kalman Filter. Asian Journal of Control, 2019, 21(4), pp. 1671-1680. DOI 10.1002/asjc.1931.

WERRIES, A. and J.M. DOLAN. Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles [online]. Pittsburgh: Car-negie-Mellon University, 2016 [viewed 2019-11-12]. DOI 10.1184/R1/6551687.v1. Available at: https://rosap.ntl.bts.gov/view/dot/36294

LIU, Y., X. FAN, C. LV, J. WU, L. LI and D. DING. An Innovative Information Fusion Method with Adaptive Kalman Filter for Integrated INS/GPS Navigation of Autonomous Vehicles. Mechanical Systems and Signal Processing, 2018, 10, pp. 605-616. DOI 10.1016/j.ymssp.2017.07.051.

SHEN, C., Y. ZHANG, J. TANG, H. CAO and J. LIU. Dual-Optimization for a MEMS-INS/GPS System during GPS Outages Based on the Cubature Kalman Filter and Neural Networks. Mechanical Systems and Signal Processing, 2019, 133, pp. 106-222. DOI 10.1016/j.ymssp.2019.07.003.

MOORE, T. and D.W. STOUCH. A Generalized Extended Kalman Filter Imple-mentation for the Robot Operating System. In: MENEGATTI, E., N. MICHAEL, K. BERNS, H. YAMAGUCHI, eds. Intelligent Autonomous Systems 13. Cham: Springer, 2015, pp. 335-348. ISBN 978-3-319-08338-4.

LI, D., X. JIA and J. ZHAO. A Novel Hybrid Fusion Algorithm for Low-Cost GPS/INS Integrated Navigation System during GPS Outages. IEEE Access, 2020, 8, pp. 53984-53996. DOI 10.1109/ACCESS.2020.2981015.

KALMAN, R.E. New Approach to Linear Filtering and Prediction Problems. Journal of Fluids Engineering, 1960, 80(1), pp. 35-45. DOI 10.1115/1.3662552.

HAO, Y., A. XU, X. SUI and Y.WANG. A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System. Sensors, 2018, 18(11), 3809. DOI 10.3390/s18113809.

SASANI, S., J. ASGARI and A.R. AMIRI-SIMKOOEI. Improving MEMS-IMU/GPS Integrated Systems for Land Vehicle Navigation Applications. GPS solutions, 2016, 20, pp. 89-100. DOI 10.1007/s10291-015-0471-3.

CARON, F., E. DUFLOS, D. POMORSKI and P. VANHEEGHE. GPS/IMU Data Fusion Using Multisensor Kalman Filtering: Introduction of Contextual Aspects. Information Fusion, 2006, 7, pp. 221-230. DOI 10.1016/j.inffus.2004.07.002.

LEE, Y., J. YOON, H. YANG, C. KIM and D. LEE. Camera-GPS-IMU Sensor Fusion for Autonomous Flying. In: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN). Vienna: IEEE, 2016, pp. 85-88. DOI 10.1109/ICUFN.2016.7536988.

WANG, S., S. DENG and G. YIN. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints. Sensors. 2016, 16(3), 280. DOI 10.3390/s16030280.

BOSTANCI, E., B. BOSTANCI, N. KANWAL and A.F. CLARK. Sensor Fusion of Camera, GPS and IMU Using Fuzzy Adaptive Multiple Motion Models. Soft Computing, 2018, 22, pp. 2619-2632. DOI 10.1007/s00500-017-2516-8

TIAN, J., Y. LIU, Y. WU, Y. LU and Z. HE. GPS/INS Fusion Algorithm Based on Variational Bayesian Adaptive Kalman Filter. International Core Journal of Engineering, 2021, 7(2), pp. 26-32. DOI 10.6919/ICJE.202102_7(2).0005.

SCHERZINGER, B.M. Precise Robust Positioning with Inertial/GPS RTK. Nav-igation, 53(2), pp. 73-83. DOI 10.1002/j.2161-4296.2000.tb00374.x.

NOURELDIN, A., T.B. KARAMAT and J. GEORGY. Fundamentals of Inertial Navigation, Satellite-Based Positioning and Their Integration. Berlin: Springer, 2013. ISBN 978-3-642-44790-7.

FARRELL, J.A., T.D. GIVARGIS and M.J. BARTH. Real-Time Differential Carrier Phase GPS-Aided INS. IEEE Transactions on Control Systems Technolo-gy, 2000, 8(4), pp. 709-721. DOI 10.1109/87.852915.

BAR-SHALOM, Y., X.-R. LI and T. KIRUBARAJAN. Estimation with Applica-tions to Tracking and Navigation: Theory Algorithms and Software. Hoboken: Wiley, 2004. ISBN 978-0-471-41655-5.