Volume 15 Number 2
April 2018
Article Contents
Derradji Nada, Mounir Bousbia-Salah and Maamar Bettayeb. Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter. International Journal of Automation and Computing, vol. 15, no. 2, pp. 207-217, 2018. doi: 10.1007/s11633-017-1065-z
Cite as: Derradji Nada, Mounir Bousbia-Salah and Maamar Bettayeb. Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter. International Journal of Automation and Computing, vol. 15, no. 2, pp. 207-217, 2018.

# Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter

Author Biography:
• Derradji Nada received the B. Sc. and M. Sc. degrees in electrical engineering from University of Bordj Bou Arreridj, Algeria in 2009 and 2011, respectively. He is currently Ph. D. degree candidate at Laboratory of Automatic and Signals Annaba (LASA), Department of Electronic Engineering, Badji Mokhtar Annaba University, Algeria.
His research interests include filtering, estimation, image compression and restoration, sensors and measurement.
ORCID iD:0000-0001-5336-2900

Maamar Bettayeb received the B. Sc., M. Sc., and Ph. D. degrees in electrical engineering from University of Southern California, USA in 1976, 1978 and 1981, respectively. He has been professor at University of Sharjah, UAE since August 2000. He also held the position of advisor to the Chancellor for Graduate Studies and Scientific Research for the years 2004/2006 and director of Research and Studies Center for the Year 2005/2006 at University of Sharjah, UAE. He was associate editor of the International Journal of Modeling, Identification and Control. He is the leader of Intelligent Systems Research Group at University of Sharjah, UAE. He has published over 300 journal and conference papers and has over 1 100 citations in recognition of his research contributions in the fields of control and signal processing. He has also supervised over 50 M. Sc. and Ph. D. students. He has been consulting for the Petrochemical Industries and has also been involved in various R&D funded projects in the areas of control and signal processing applications.
His research interests include H control, rational approximation, signal and image processing, process control, networked control systems, fractional dynamics and control, nonlinear estimation and filtering, software computing, wavelets, renewable energies and engineering education.
E-mail:maamar@sharjah.ac.ae

• Corresponding author: Mounir Bousbia-Salah received the B. Eng. degree in electronics from Annaba university, Algeria in 1984, the M. Sc. degree in electronics from Cardiff University, UK in 1988, and the Ph. D. degree in electronics from BADJI Mokhtar Annaba university, Algeria in 2004. He is a full professor and director of research with the Department of Electronic Engineering, BADJI Mokhtar Annaba University, Algeria. He is also head of research team in biomedical engineering with the Laboratory of Automatic and Signals of Annaba (LASA). He has been a reviewer with IEEE Sensors Journal, Instrumentation Science and Technology, Measurement Science and Technology, Disability and Rehabilitation, Journal of Intelligent and Robotic Systems, Industrial Robot, Sensors and Transducers Journal, Journal of Electrical and Electronics Engineering Research and many international conferences. He is also member of the International Frequency Sensor Association and affiliated with IFAC. He has published more than 50 journal and conference papers and one book chapter.
His research interests include biomedical electronics, manmachine communication, sensors and control.
E-mail:bousbia.salah@univ-annaba.org (Corresponding author)
ORCID iD:0000-0002-2611-9670
• Accepted: 2016-05-11
• Published Online: 2017-04-19
• This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy measurements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter (UKF). Two data fusion architectures:measurement fusion (MF) and state vector fusion (SVF) are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures.
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• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures (15)  / Tables (3)

## Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter

• ###### Corresponding author:Mounir Bousbia-Salah received the B. Eng. degree in electronics from Annaba university, Algeria in 1984, the M. Sc. degree in electronics from Cardiff University, UK in 1988, and the Ph. D. degree in electronics from BADJI Mokhtar Annaba university, Algeria in 2004. He is a full professor and director of research with the Department of Electronic Engineering, BADJI Mokhtar Annaba University, Algeria. He is also head of research team in biomedical engineering with the Laboratory of Automatic and Signals of Annaba (LASA). He has been a reviewer with IEEE Sensors Journal, Instrumentation Science and Technology, Measurement Science and Technology, Disability and Rehabilitation, Journal of Intelligent and Robotic Systems, Industrial Robot, Sensors and Transducers Journal, Journal of Electrical and Electronics Engineering Research and many international conferences. He is also member of the International Frequency Sensor Association and affiliated with IFAC. He has published more than 50 journal and conference papers and one book chapter.     His research interests include biomedical electronics, manmachine communication, sensors and control.     E-mail:bousbia.salah@univ-annaba.org (Corresponding author)     ORCID iD:0000-0002-2611-9670

Abstract: This paper investigates the problem of estimation of the wheelchair position in indoor environments with noisy measurements. The measuring system is based on two odometers placed on the axis of the wheels combined with a magnetic compass to determine the position and orientation. Determination of displacements is implemented by an accelerometer. Data coming from sensors are combined and used as inputs to unscented Kalman filter (UKF). Two data fusion architectures:measurement fusion (MF) and state vector fusion (SVF) are proposed to merge the available measurements. Comparative studies of these two architectures show that the MF architecture provides states estimation with relatively less uncertainty compared to SVF. However, odometers measurements determine the position with relatively high uncertainty followed by the accelerometer measurements. Therefore, fusion in the navigation system is needed. The obtained simulation results show the effectiveness of proposed architectures.

Derradji Nada, Mounir Bousbia-Salah and Maamar Bettayeb. Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter. International Journal of Automation and Computing, vol. 15, no. 2, pp. 207-217, 2018. doi: 10.1007/s11633-017-1065-z
 Citation: Derradji Nada, Mounir Bousbia-Salah and Maamar Bettayeb. Multi-sensor Data Fusion for Wheelchair Position Estimation with Unscented Kalman Filter. International Journal of Automation and Computing, vol. 15, no. 2, pp. 207-217, 2018.
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