Research Papers

Vehicle Positioning Based on Velocity and Heading Angle Observer Using Low-Cost Sensor Fusion

[+] Author and Article Information
Giseo Park

Department of Mechanical Engineering,
291 Daehak-ro, Yuseong-gu,
Daejeon 34141, South Korea
e-mail: giseo123@kaist.ac.kr

Yoonjin Hwang

Department of Mechanical Engineering,
291 Daehak-ro, Yuseong-gu,
Daejeon 34141, South Korea
e-mail: yoonjinh@kaist.ac.kr

Seibum B. Choi

Department of Mechanical Engineering,
291 Daehak-ro, Yuseong-gu,
Daejeon 34141, South Korea
e-mail: sbchoi@kaist.ac.kr

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 3, 2017; final manuscript received May 10, 2017; published online August 10, 2017. Assoc. Editor: Zongxuan Sun.

J. Dyn. Sys., Meas., Control 139(12), 121008 (Aug 10, 2017) (13 pages) Paper No: DS-17-1002; doi: 10.1115/1.4036881 History: Received January 03, 2017; Revised May 10, 2017

The vehicle positioning system can be utilized for various automotive applications. Primarily focusing on practicality, this paper presents a new method for vehicle positioning systems using low-cost sensor fusion, which combines global positioning system (GPS) data and data from easily available in-vehicle sensors. As part of the vehicle positioning, a novel nonlinear observer for vehicle velocity and heading angle estimation is designed, and the convergence of estimation error is also investigated using Lyapunov stability analysis. Based on this estimation information, a new adaptive Kalman filter with rule-based logic provides robust and highly accurate estimations of the vehicle position. It adjusts the noise covariance matrices Q and R in order to adapt to various environments, such as different driving maneuvers and ever-changing GPS conditions. The performance of the entire system is verified through experimental results using a commercial vehicle. Finally, through a comparative study, the effectiveness of the proposed algorithm is confirmed.

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Jo, K. , Lee, M. , and Sunwoo, M. , 2016, “ Road Slope Aided Vehicle Position Estimation System Based on Sensor Fusion of GPS and Automotive Onboard Sensors,” IEEE Trans. Intell. Transp. Syst., 17(1), pp. 250–263. [CrossRef]
Jo, K. , Chu, K. , and Sunwoo, M. , 2012, “ Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning,” IEEE Trans. Intell. Transp. Syst., 13(1), pp. 329–343. [CrossRef]
Bevly, D. M. , 2004, “ Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles,” ASME J. Dyn. Syst., Meas., Control, 126(2), pp. 255–264. [CrossRef]
Li, X. , Chen, W. , and Chan, C. , 2014, “ A Reliable Multisensory Fusion Strategy for Land Vehicle Positioning Using Low Cost Sensors,” Proc. Inst. Mech. Eng., Part D, 228(12), pp. 1375–1397. [CrossRef]
Wu, Z. , Yao, M. , Ma, H. , and Jia, W. , 2013, “ Improving Accuracy of the Vehicle Attitude Estimation for Low-Cost INS/GPS Integration Aided by the GPS-Measured Course Angle,” IEEE Trans. Intell. Transp. Syst., 14(2), pp. 553–564. [CrossRef]
Yang, Y. , Gu, Z. , and Hu, L. , 2007, “ Research on the Information Fusion Method of the Global Positioning System-Dead Reckoning Vehicle Integrated Navigation System,” Proc. Inst. Mech. Eng., Part D, 221(5), pp. 543–553. [CrossRef]
Fiengo, G. , Domenico, D. D. , and Glielmo, L. , 2009, “ A Hybrid Procedure Strategy for Vehicle Localization System: Design and Prototyping,” Control Eng. Pract., 17(1), pp. 14–25. [CrossRef]
Bonnabel, S. , and Salaun, E. , 2011, “ Design and Prototyping of a Low-Cost Vehicle Localization System With Guaranteed Convergence Properties,” Control Eng. Pract., 19(6), pp. 591–601. [CrossRef]
Mehra, R. , 1970, “ On the Identification of Variances and Adaptive Kalman Filtering,” IEEE Trans. Autom. Control, 15(2), pp. 175–184. [CrossRef]
Oh, J. J. , and Choi, S. B. , 2012, “ Vehicle Velocity Observer Design Using 6-D IMU and Multiple-Observer Approach,” IEEE Trans. Intell. Transp. Syst., 13(4), pp. 1865–1879. [CrossRef]
Oh, J. J. , and Choi, S. B. , 2013, “ Dynamic Sensor Zeroing Algorithm of 6D IMU Mounted on Ground Vehicles,” Int. J. Automot. Technol., 14(2), pp. 221–231. [CrossRef]
Oh, J. J. , and Choi, S. B. , 2013, “ Vehicle Roll and Pitch Angle Estimation Using a Cost-Effective Six-Dimensional Inertial Measurement Unit,” Proc. Inst. Mech. Eng., Part D, 227(4), pp. 577–590. [CrossRef]
Meguro, J. , Kojima, Y. , Suzuki, N. , and Teramoto, E. , 2012, “ Positioning Technique Based on Vehicle Trajectory Using GPS Raw Data and Low-Cost IMU,” Int. J. Automot. Eng., 3(2), pp. 75–80.
Yoon, J. H. , Li, S. E. , and Ahn, C. , 2016, “ Estimation of Vehicle Sideslip Angle and Tire-Road Friction Coefficient Based on Magnetometer With GPS,” Int. J. Automot. Technol., 17(3), pp. 427–435. [CrossRef]
Langley, R. B. , 1999, Dilution of Precision, GPS World, Fredericton, NB, Canada.
Leung, K. T. , Whidborne, J. F. , Purdy, D. , and Barber, P. , 2011, “ Road Vehicle Sate Estimation Using Low-Cost GPS/INS,” Mech. Syst. Signal Process., 25(6), pp. 1988–2004. [CrossRef]
Franklin, G. F. , Powell, J. D. , and Naeini, A. E. , 2005, Feedback Control of Dynamic Systems, 6th ed., Prentice Hall, Upper Saddle River, NJ.
Hermann, R. , and Krener, A. J. , 1977, “ Nonlinear Controllability and Observability,” IEEE Trans. Autom. Control, 22(5), pp. 728–740. [CrossRef]
Stephant, J. , Charara, A. , and Meizel, D. , 2007, “ Evaluation of a Sliding Mode Observer for Vehicle Sideslip Angle,” Control Eng. Pract., 15(7), pp. 803–812. [CrossRef]
Travers, M. , and Choset, H. , 2015, “ Use of the Nonlinear Observability Rank Condition for Improved Parametric Estimation,” IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, May 26–30, pp. 1029–1035.
Simon, D. , 2006, Optimal State Estimation, Wiley, New York. [CrossRef]
Khalil, H. , 2002, Nonlinear System, 3rd ed., Prentice Hall, Upper Saddle River, NJ.
Almagbile, A. , Wang, J. , and Ding, W. , 2010, “ Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration,” J. Global Positioning Syst., 9(1), pp. 33–40. [CrossRef]
Ding, W. , Wang, J. , and Rizos, C. , 2007, “ Improving Adaptive Kalman Estimation in GPS/INS Integration,” J. Navig., 60(3), pp. 517–529. [CrossRef]
Mohamed, A. H. , and Schwarz, K. P. , 1999, “ Adaptive Kalman Filtering for INS/GPS,” J. Geod., 73(4), pp. 193–203. [CrossRef]
Yoon, J. , and Peng, H. , 2014, “ A Cost-Effective Sideslip Estimation Method Using Velocity Measurements From Two GPS Receivers,” IEEE Trans. Veh. Technol., 63(6), pp. 2589–2599. [CrossRef]
Han, K. , Hwang, Y. , Lee, E. , and Choi, S. B. , 2016, “ Robust Estimation of Maximum Tire-Road Friction Coefficient Considering Road Surface Irregularity,” Int. J. Automot. Technol., 17(3), pp. 415–425. [CrossRef]


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Fig. 1

Flowchart of the proposed vehicle positioning system

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Fig. 3

Vehicle kinematic model

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Fig. 4

Estimation results obtained from the nonlinear observer

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Fig. 5

Illustration of stability

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Fig. 6

Kalman filter process

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Fig. 7

Number of satellites, HDOP, and measurement error of the GPS

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Fig. 8

Test drive course (captured from Google Earth)

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Fig. 9

Adaptive Kalman filter: (a) GPS mode, (b) online estimation of Q, and (c) online estimation of R

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Fig. 11

Velocity and heading angle estimation results

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Fig. 12

Heading angle estimation with bad initialization errors

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Fig. 13

Vehicle positioning results during one cycle

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Fig. 14

Each Euclidean distance error: (a) KFs based on the GPS data and (b) KFs based on the nonlinear observer



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