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Original Article

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Int. J. Fuzzy Log. Intell. Syst. -0001; 17(2): 121-128

Published online November 30, -0001

https://doi.org/10.5391/IJFIS.2017.17.2.121

© The Korean Institute of Intelligent Systems

A Position and Velocity Estimation Using Multifarious and Multiple Sensor Fusion

Youngwan Cho1, and Heejin Lee2

1Department of Computer Engineering, Seokyeong University, Seoul, Korea, 2Department of Electrical, Electronic, and Control Engineering, Hankyong National University, Ansung, Korea

Correspondence to :
Heejin Lee, (lhjin@hknu.ac.kr)

Received: May 18, 2017; Revised: June 23, 2017; Accepted: June 23, 2017

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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In this paper, we propose a fusion algorithm of multifarious and multiple sensors to enhance the accuracy and reliability of position and velocity estimation for the vehicles. We proposed an adaptive Kalman filter for multiple sensor fusion to provide a fault tolerant estimation. We verified the multiple sensor fusion estimator can provide a fault tolerant estimation through Matlab simulation and laboratory equipped experiments. We also proposed a fusion algorithm of multifarious sensors in order to enhance the velocity estimation accuracy. We proposed a Kalman filter error correction for compensate the accumulative error in the main sensor with the other type of sensor which has characteristic of biased error. We also developed a fusion algorithm for compensate the error in the position measuring with the velocity measuring. We made experiments for estimating position and velocity of vehicle simultaneously through the fusion of multifarious and multiple sensors and showed that average position error was 1.5764 m and average velocity accuracy was 99.81%.

Keywords: Position estimation, Velocity estimation, Kalman filter, Sensor fusion, Map based GPS error correction

Conflict of Interest

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No potential conflict of interest relevant to this article was reported.

Biographies

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Youngwan Cho was born in Hamyang, Korea, in 1968. He received the B.S., M.S., and Ph.D. degrees in electronic engineering from Yonsei University, Seoul, Korea, in 1991, 1993, and 1999, respectively. He worked as a Senior Research Engineer in the Control System Group at SAMSUNG Electronics, Seoul, Korea, from 1999 to 2003. He was a visiting scholar at Department of Mechanical Engineering, Michigan State University from 2016 to 2017. He is currently working as an Associate Professor in the Department of Computer Engineering, Seokyeong University, Seoul, Korea. His research interests include fuzzy control theory and applications, intelligent control systems, machine learning, and robotics and automation.

E-mail: ywcho@skuniv.ac.kr


Heejin Lee received the B.S., M.S., and Ph.D. degrees in electronic engineering from Yonsei University, Seoul, Korea, in 1987, 1989, and 1998, respectively. He worked as a Senior Research Engineer in the Daewoo Telecom Ltd., Seoul, Korea, from 1989 to 1993. He is currently working as a Professor in the Department of Electrical, Electronic and Control Engineering, Hankyong National University, Ansung, Korea. His current research interests include fuzzy control theory, fuzzy application system, adaptive and robust control, and intelligent robotics.

E-mail: lhjin@hknu.ac.kr


Article

Original Article

Int. J. Fuzzy Log. Intell. Syst. -0001; 17(2): 121-128

Published online November 30, -0001 https://doi.org/10.5391/IJFIS.2017.17.2.121

Copyright © The Korean Institute of Intelligent Systems.

A Position and Velocity Estimation Using Multifarious and Multiple Sensor Fusion

Youngwan Cho1, and Heejin Lee2

1Department of Computer Engineering, Seokyeong University, Seoul, Korea, 2Department of Electrical, Electronic, and Control Engineering, Hankyong National University, Ansung, Korea

Correspondence to:Heejin Lee, (lhjin@hknu.ac.kr)

Received: May 18, 2017; Revised: June 23, 2017; Accepted: June 23, 2017

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this paper, we propose a fusion algorithm of multifarious and multiple sensors to enhance the accuracy and reliability of position and velocity estimation for the vehicles. We proposed an adaptive Kalman filter for multiple sensor fusion to provide a fault tolerant estimation. We verified the multiple sensor fusion estimator can provide a fault tolerant estimation through Matlab simulation and laboratory equipped experiments. We also proposed a fusion algorithm of multifarious sensors in order to enhance the velocity estimation accuracy. We proposed a Kalman filter error correction for compensate the accumulative error in the main sensor with the other type of sensor which has characteristic of biased error. We also developed a fusion algorithm for compensate the error in the position measuring with the velocity measuring. We made experiments for estimating position and velocity of vehicle simultaneously through the fusion of multifarious and multiple sensors and showed that average position error was 1.5764 m and average velocity accuracy was 99.81%.

Keywords: Position estimation, Velocity estimation, Kalman filter, Sensor fusion, Map based GPS error correction

Fig 1.

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Figure 1.

Multiple sensor fusion model.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 2.

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Figure 2.

Simulated sensor profiles.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 3.

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Figure 3.

Simulation result of sensor fusion.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 4.

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Figure 4.

Multifarious sensor fusion model.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 5.

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Figure 5.

Simulation result of multifarious sensor model.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 6.

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Figure 6.

Approximation of moving pattern and the α.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 7.

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Figure 7.

Measurement error of the compared map matching algorithms. (a) Point-to-curve matching, (b) GPS error filter matching.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 8.

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Figure 8.

Block diagram of the position and velocity estimator.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 9.

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Figure 9.

Experimented navigation route.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

Fig 10.

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Figure 10.

Experimental results of position estimating.

The International Journal of Fuzzy Logic and Intelligent Systems -0001; 17: 121-128https://doi.org/10.5391/IJFIS.2017.17.2.121

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