Implementation of GPS / INS Fusion Algorithm Using GPS Pseudo-Range

Document Type : Dynamics, Vibrations, and Control

Authors

1 Associate Professor, Faculty of Electrical and Computer Engineering, Malek-e-Ashtar University of Technology, Tehran, Iran

2 Corresponding author: Assistant Professor, Faculty of Electrical and Computer Engineering, Malek-e-Ashtar University of Technology, Tehran, Iran

3 M.Sc. Student, Faculty of Electrical and Computer Engineering, Malek-e-Ashtar University of Technology, Tehran, Iran

Abstract

The navigation system of vehicles calculates the speed, position and attitude of the moving device relative to a reference frame and provides it to the guidance system. One of the most widely used navigation systems is the inertial navigation system. Due to the increasing error of the navigation system over time, the integrated navigation system is usually used for long-term navigation. One of the most common integrated navigation systems is the INS integrated navigation system with GPS, each of them which has advantages and disadvantages that cover the other. In this paper, two GPS and INS data integration algorithms with loosely and tightly coupled integration are implemented and compared. In the loosely coupled method, GPS measurements include positions and speeds. In the tightly coupled method, a model for GPS error is considered, which includes bias dynamics and GPS clock drift. The result of combining GPS and INS data in this way is closer to the truth, but in the method of loosely coupled, the result of the combination follows the average of GPS data. In the implementation of the combined algorithm with tightly coupled, raw GPS data is used, which is pseudo-range and pseudo-range rate along with astronomical information. In this paper, an extended Kalman filter is used to integrate the data of two measurement data. The simulation results show the superiority of tightly over loosely connection performance. Also, the integration algorithm with a loosely approach has been implemented in hardware and car testing has been done in two scenarios of connecting and disconnecting GPS.

Keywords


Smiley face

[1] Jekeli C. Inertial navigation systems with geodetic applications.  Inertial Navigation Systems with Geodetic Applications: de Gruyter; 2012.##
[2] Norouz M, Ebrahimi M, Arbabmir M. A Review of Integrated Navigation System GPS/INS Methods and Study of New Approaches in This Field. Journal of Mechanical Engineering. 2018;48(3):365-9..##
[3] M. Norouzi ME, and M. Arbabmir. A review of newly developed algorithms in GPS/INS integration. Int Aerosp Conf iran. 2017.##
[4] Wang M, Wu W, Zhou P, He X. State transformation extended Kalman filter for GPS/SINS tightly coupled integration. Gps Solutions. 2018;22(4):1-12.##
[5] Li T, Zhang H, Gao Z, Chen Q, Niu X. High-accuracy positioning in urban environments using single-frequency multi-GNSS RTK/MEMS-IMU integration. Remote sensing. 2018;10(2):205.##
[6] Li W, Li W, Cui X, Zhao S, Lu M. A Tightly Coupled RTK/INS Algorithmwith Ambiguity Resolution in the Position Domain for Ground Vehicles in Harsh Urban Environments. Sensors (14248220). 2018;18(7).##
[7] Tawk Y, Tomé P, Botteron C, Stebler Y, Farine P-A. Implementation and performance of a GPS/INS tightly coupled assisted PLL architecture using MEMS inertial sensors. Sensors. 2014;14(2):3768-96.##
[8] Wendel J, Trommer GF. Tightly coupled GPS/INS integration for missile applications. Aerospace Science and Technology. 2004;8(7):627-34.##
[9] Falco G, Pini M, Marucco G. Loose and tight GNSS/INS integrations: Comparison of performance assessed in real urban scenarios. Sensors. 2017;17(2):255.##
[10] Houzeng H, Jian W, Mingyi D. GPS/BDS/INS tightly coupled integration accuracy improvement using an improved adaptive interacting multiple model with classified measurement update. Chinese Journal of Aeronautics. 2018;31(3):556-66.##
[11] Zhao X, Li J, Yan X, Ji S. Robust adaptive cubature Kalman filter and its application to ultra-tightly coupled SINS/GPS navigation system. Sensors. 2018;18(7):2352.##
[12] Chiang K-W, Tsai G-J, Chang H, Joly C, Ei-Sheimy N. Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme. Information Fusion. 2019;50:181-96.##
[13] Zhang P, Hancock CM, Lau L, Roberts GW, de Ligt H. Low-cost IMU and odometer tightly coupled integration with Robust Kalman filter for underground 3-D pipeline mapping. Measurement. 2019;137:454-63.##
[14] Xie F, Sun R, Kang G, Qian W, Zhao J, Zhang L. A jamming tolerant BeiDou combined B1/B2 vector tracking algorithm for ultra-tightly coupled GNSS/INS systems. Aerospace Science and Technology. 2017;70:265-76.##
[15] Gao Z, Ge M, Shen W, Li Y, Chen Q, Zhang H, et al. Evaluation on the impact of IMU grades on BDS+ GPS PPP/INS tightly coupled integration. Advances in Space Research. 2017;60(6):1283-99.##
[16] Nguyen H-D, Nguyen V-H, Nguyen H-V, editors. Tightly-coupled INS/GPS integration with magnetic aid. 2017 2nd International Conference on Control and Robotics Engineering (ICCRE); 2017: IEEE.##
[17] Li Z, Chang G, Gao J, Wang J, Hernandez A. GPS/UWB/MEMS-IMU tightly coupled navigation with improved robust Kalman filter. Advances in Space Research. 2016;58(11):2424-34.##
[18] Tseng C-H, Lin S-F, Jwo D-J. Fuzzy adaptive cubature Kalman filter for integrated navigation systems. Sensors. 2016;16(8):1167.##
[19] A. Ramezani JZPD, and M. S. Ph.D. Performance improvement of the inertial navigation system using unscented and cubature Kalman filters. Shiraz Univ Technol. 2013.##
[20] Nazemipour A, Manzuri MT, Kamran D, Karimian M. MEMS gyro bias estimation in accelerated motions using sensor fusion of camera and angular-rate gyroscope. IEEE Transactions on Vehicular Technology. 2020;69(4):3841-51.##
[21] M. Alinezhad DHK, and Dr. Gholamreza Akbarizadeh. Design and construction inertial navigation system. Shahid Chamran Univ Ahvaz,. 2015.##
[22] N. Moazzen DHK, and D. K. Ansari. Navigation Accuracy Enhancement of Integrated GPS/INS System using Nonlinear SDRE Filtering. Shahid Chamran Univ Ahvaz,. 2016.##
[23] M. M. H. Darani DHN, and D. H. G. Asl. Nonlinear Filters Applied to Tightly Coupled Integration of INS/GPS/PL for Precise Navigation in Automatic Landing of UAVs. Sharif Univ Technol. 2015.##
[24] Ren J, Zi J, Guan H, Li J, editors. Design of an Ultra-Tightly Coupled Integrated INS/GPS Navigation System Based on UPF. 2020 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS); 2020: IEEE.##
[25] Gao B, Hu G, Zhong Y, Zhu X. Cubature Kalman filter with both adaptability and robustness for tightly-coupled GNSS/INS integration. IEEE Sensors Journal. 2021;21(13):14997-5011.##
[26] Zhang J, Zhang T, Jiang X, Wang S, editors. Tightly coupled GPS/INS integrated navigation algorithm based on Kalman filter. 2012 Second International Conference on Business Computing and Global Informatization; 2012: IEEE.##
Volume 18, Issue 4 - Serial Number 70
Serial No. 70, Winter Quarterly
December 2022
Pages 105-118
  • Receive Date: 23 July 2022
  • Revise Date: 24 August 2022
  • Accept Date: 12 September 2022
  • Publish Date: 23 October 2022