EKF Based Fusion Techniques Applied to Wheelchair Navigation System

Author(s): Derradji Nada, Mounir Bousbia-Salah*, Maamar Bettayeb*

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 12 , Issue 4 , 2019

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: The aim of this paper is to investigate data fusion techniques based on an Extended Kalman Filter (EKF), and more specifically, the nonlinear dynamic estimation of a wheelchair navigation system.

Methods: Three data fusion techniques are presented and a comparison between them is studied. It combines the noisy measurement data coming from several sensors to obtain the best estimate of position while reducing the measurement uncertainties.

Results: By using the MATLAB, the performance of these techniques is checked with simulated data and performance metrics are calculated for evaluation of the algorithms. Detailed mathematical expressions are provided which could be useful for algorithm implementation.

Conclusion: The results show that the algorithm based on a measurement fusion technique gives a good estimate when compared with another one.

Keywords: Data fusion, extended kalman filter, hybrid fussion, measurement fussion, state vector fussion, wheelchair navigation system.

J.A. Farrell, Aided navigation: GPS with high rate sensors., McGraw-Hill, Inc., 2008.
C. Fritsche, A. Klein, and D. Wurtz, "Hybrid GPS/GSM localization of mobile terminals using the extended Kalman filter, In:", 6th Work. Positioning, Navig. Commun. Hannover, Germany, 2009, pp. 189–194.
B. Barshan, and H.F. Durrant-Whyte, "Inertial navigation systems for mobile robots", IEEE Trans. Robot. Autom.. vol. 11, pp. 328- 342, 1995.
J.A.F. Cheng, Y. Lu, and E.R. Thomas, "Data fusion via Kalman filter: GPS and INS autonomous mobile robots", Taylor & Francis Group. LLC, 2006.
P.A. Miller, J.A. Farrell, Y. Zhao, and V. Djapic, "Autonomous underwater vehicle navigation", IEEE J. Oceanic Eng.. vol. 35, pp. 663-678, 2010.
Y. Bar-Shalom, and L. Campo, "The effect of the common process noise on the two-sensor fused-track covariance", IEEE Trans. Aerosp. Electron. Syst.. vol. AES-22, pp. 803-805, 1986.
J.A. Roecker, and C.D. McGillem, "Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion", IEEE Trans. Aerosp. Electron. Syst.. vol. 24, pp. 447-449, 1988.
R.K. Saha, "Track-to-track fusion with dissimilar sensors", IEEE Trans. Aerosp. Electron. Syst.. vol. 32, pp. 1021-1029, 1996.
K.C. Chang, R.K. Saha, and Y. Bar-Shalom, "On optimal track-to-track fusion", IEEE Trans. Aerosp. Electron. Syst.. vol. 33, pp. 1271–1276, 1997.
J.B. Gao, and C.J. Harris, "Some remarks on Kalman filters for the multisensor fusion", Inf. Fusion. vol. 3, pp. 191-201, 2002.
L. Zhang, Q. Cheng, Y. Wang, and S. Zeadally, "Landscape: A high performance distributed positioning scheme for outdoor sensor networks, In:", Wireless And Mobile Computing, Networking And Communications, 2005.(WiMob’2005), IEEE International Conference on. Montreal, Que., Canada, 2005, pp. 430-437.
L. Zhang, X. Zhou, and Q. Cheng, "Landscape-3D: A robust localization scheme for sensor networks over complex 3D terrains, In:", Local Computer Networks, Proceedings 2006 31st IEEE Conference on. Tampa, FL, USA, 2006, pp. 239-246.
L. Zhang, Q. Cheng, Y. Wang, and S. Zeadally, "A novel distributed sensor positioning system using the dual of target tracking", IEEE Trans. Comput.. vol. 57, pp. 246-260, 2008.
L. Zhang, and Q. Cheng, "Landscape (T): A robust and low-cost sensor positioning system using the dual of target tracking, In:", Poster Proc. IEEE/ACM Int’l Conf. Distributed Computing in Sensor Systems (DCOSS’06), June. 2006.
V.P.S. Naidu, "Fusion rrchitectures for 3D target tracking using Irst and radar measurements", J. Aerosp. Sci. Technol., vol. 62, no. 3, pp. 184-195, 2010.
D. Nada, M.B. Salah, M. Bousbia-Salah, and M. Bettayeb, "Fusion architectures with extended KALMAN filter for locate wheelchair position using sensors measurements, In", Proceedings of the IEEE. 2014 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM). Tunis, Tunisia, 2014, pp. 1– 7.
E.A. Wan, and R. Van Der Merwe, "The unscented Kalman filter for nonlinear estimation,” In", Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373). Lake Louise, Alberta, Canada, 2000, pp. 153–158.
I. Arasaratnam, and S. Haykin, "Square-root quadrature kalman filtering", IEEE Trans. Signal Process.. vol. 56, pp. 2589-2593, 2008.
I. Arasaratnam, S. Haykin, and R.J. Elliott, "Discrete-time nonlinear filtering algorithms Using Gauss-Hermite Quadrature", Proc. IEEE. vol. 95, pp. 953-977, 2007.
I. Arasaratnam, S. Haykin, and T.R. Hurd, "Cubature kalman filtering for continuous-discrete systems: Theory and simulations", IEEE Trans. Signal Process., vol. 58, no. 10, pp. 4977-4993, 2010.
B.D.O. Anderson, J.B. Moore, and M. Eslami, "Optimal Filtering", IEEE Trans. Syst. Man Cybern.. vol. 12, pp. 235-236, 1982.
V. Awasthi, and K. Raj, "“A survey on the algorithms of kalman filter,” VSRD Int. J. Tech", Non-Technical Res.. vol. 2, pp. 73-88, 2011.
D.L. Hall, and S.A.H. McMullen, Mathematical Techniques in Multisensor Data Fusion. Artech House, 2004.
J.R. Raol, Multi-Sensor Data Fusion with MATLAB. 1st ed. Boca Raton, FL, USA: CRC Press, 2009.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Published on: 22 August, 2019
Page: [304 - 316]
Pages: 13
DOI: 10.2174/1570179415666180709125132
Price: $25

Article Metrics

PDF: 15