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

Yoonjin Hwang

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

Seibum B. Choi

Professor
Mem. ASME
Department of Mechanical Engineering,
KAIST,
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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Figures

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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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