Integrating SINS Sensors with Odometer Measurements for Land Vehicle Navigation System

Susu Fang; Zengcai Wang; Jiacheng Fan

doi:10.6180/jase.201906_22(2).0009

Integrating SINS Sensors with Odometer Measurements for Land Vehicle Navigation System

Susu Fang¹, Zengcai Wang This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Jiacheng Fan¹

¹Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Ministry of Education, College of Mechanical Engineering, Shandong University, Jinan, P.R. China

Received: April 9, 2018
Accepted: December 7, 2018
Publication Date: June 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201906_22(2).0009

ABSTRACT

This paper details a new model for land vehicle navigation including attitude, velocity, and position estimation using an odometer (OD) and a magnetometer (MAG) in conjunction with strap down inertial navigation system (SINS) sensors. In this new model, the SINS/OD navigation system is designed firstly. In SINS/OD system, the application of non-holonomic constraints for obtaining virtual velocity measurements is investigated and an innovation-based estimation adaptive (IAE) Kalman filter is designed to provide a land vehicle navigation solution by combining the velocity measurement from an odometer and error in the centripetal acceleration difference as a new observable from SINS sensors. In addition, an adaptive federated Kalman filter (AFKF) is designed, which integrates the magnetometer on the basis of the SINS/OD navigation system. It is shown that the accuracy of the new method is higher than of SINS, and using it with the magnetometer in the integrated navigation system yields more accurate estimations. Finally, field tests show that the SINS/OD/MAG system improves the precision of the navigation system and promotes a fault-tolerant performance. The proposed approach can be used as a backup navigation system during outages of the Global Positioning System (GPS) for an extended amount of time with the navigation error noticeably bounded. The redundancy and integrity of the entire navigation system are also further enhanced.

Keywords: Navigation System Model, Vehicle Integrating Navigation, Kalman Filter, Fault-tolerant performance

REFERENCES

[1] Titterton,D., J.L.Weston, and J.Weston(2004) Strapdown Inertial Navigation Technology 17, IET.
[2] Cai, C. L., L. Yi, and Y. W. Liu (2009) Status quo and trend of inertial integrated navigation system based on MEMS, Journal of Chinese Inertial Technology 17, 562567.
[3] Yu, G.-M., J. Xiong, H. Guo, and J.-X. Wang (2014) GNSS/INS/VKM vehicle integrated navigation system, China Satellite Navigation Conference (CSNC), 2014 Proceedings: VolumeIII, 585594. doi:10.1007/ 978-3-642-54740-9_51
[4] Morgado, M., P. Batista, P. Oliveira, and C. Silvestre (2011) Position USBL/DVL sensor-based navigation filter in the presence of unknown ocean currents, Automatica 47(12), 26042614. doi: 10.1016/j.automatica. 2011.09.024
[5] Zhou, B., and X. Cheng (2010) Federated filtering algorithm based on fuzzy adaptive UKF for marine SINS/GPS/DVL integrated system, Chinese Control and Decision Conference, 20822085. doi: 10.1109/ CCDC.2010.5498869
[6] Peng, D., and X. Nie (2009) The low cost MINS/GPS integrated navigation system’s design and application, Chinese Journal of Sensors & Actuators.
[7] Li, X., and Q. Xu (2016) A reliable fusion positioning strategy for land vehicles in GPS-denied environments based on low-cost sensors, IEEE Transactions on Industrial Electronics PP, 11.
[8] Wang, M., and Y. Fu (2009) Data fusion of ALV GPS/ DR integrated navigation system based on BP neural network, IEEE International Conference on Industrial Technology, 14. doi: 10.1109/ICIT.2009.4939595
[9] Cai, C. L., Y. Liu, and Y. W. Liu (2009) Status quo and trend of inertial integrated navigation system based on MEMS, Journal of Chinese Inertial Technology 17, 562567.
[10] Liu, H., Z. Wang, S. Fang, and C. Li (2017) MEMS based SINS/OD filter for land vehicles’ applications, Mathematical Problems in Engineering 2017, 19. doi: 10.1155/2017/1691320
[11] Castagnetti, C., L. Biagi, and A. Capra (2011) Design of a low-cost GPS/magnetometer system for landbased navigation: integration and autocalibration algorithms, In FIG Working Week 2011-Bridging the Gap between Cultures, 115.
[12] Thompson, M. J., M. Li, and D. A. Horsley (2011) Low power 3-axis Lorentz force navigation magnetometer, IEEE International Conference on MICRO Electro Mechanical Systems, 593596. doi: 10.1109/ MEMSYS.2011.5734494
[13] Liu, C. S. (2012) A globally optimal iterative algorithm to solve an ill-posed linear system, Computer Modeling in Engineering & Sciences 84, 383403.
[14] Bonnabel, S., P. Martin, and E. Salaun (2009) Invariant extended Kalman filter: theory and application to a velocity-aided attitude estimation problem, Decision and Control, 2009 Held Jointly with the 2009 Chinese Control Conference. Cdc/ccc 2009. Proceedings of the IEEE Conference on, 12971304.
[15] Hemerly, E. M., and V. R. Schad (2005) Real-time implementation of a low cost INS/GPS Kalman filter based navigation system, International Congress of Mechanical Engineering, Ouro Preto.
[16] Bo-Wen, L. I., D. Y. Yao, D. O. Automation, and T. University (2014) Low-cost MEMS IMU navigation positioning method for land vehicle, Journal of Chinese Inertial Technology.
[17] Wu, Y., C. Goodall, and N. El-Sheimy(2010) Self-calibration for IMU/odometer land navigation: simulation and test results, In Proceedings of the ION International Technical Meeting, San Diego, CA, USA, 329334.
[18] Nassar, S., and N. El-Sheimy(2006) Acombined algorithm of improving INS error modeling and sensor measurements for accurate INS/GPS navigation, Gps Solutions 10(1), 2939. doi: 10.1007/s10291-0050149-3
[19] Dah-Jing, J. and T. P. Weng (2008) An adaptive sensor fusion method with applications in integrated navigation, Journal of Navigation 61(4), 705721. doi: 10.1017/S0373463308004827
[20] Loebis, D., R. Sutton, J. Chudley, and W. Naeem (2004) Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system, Control Engineering Practice 12(12), 15311539. doi: 10.1016/j.conengprac.2003.11.008
[21] Sokolovic, V. S., G. Dikic, and R. Stancic (2013) Integration of INS, GPS, magnetometer and barometer for improving accuracy navigation of the vehicle, Defence Science Journal 63, 451455. doi: 10.14429/dsj.63. 4534
[22] Coco, S., G. Chisari, P. D. Falco, E. Iraci, S. Militello, and A. Laudani (2014) Accurate estimation of vehicle attitude for satellite tracking in Ka Band SOTM, European Modelling Symposium, 409414. doi: 10.1109/ EMS.2014.57