Abstract
An efficient approach for deriving accurate pose and heading values through multi-sensor fusion of data from several inexpensive sensors (such as multiple GPS (Global Positioning Systems), EC (electronic compass), rate gyro) is presented. The proposed multisensor fusion approach is composed of several sub-methods namely initial heading calculation, classification and weighing (CnW), extended Kalman filter (EKF) and then covariance intersection (CI) algorithms. The consecutive implementation of the sub-methods gives an accurate heading value with lesser RMSE (root mean square error) compared to the original GPS COG (course over ground) and EC. Several experimental tests were done to confirm the good performance of the proposed process.
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Zhang, Y. L., Park, J. H., Sel, N. O., and Chong, K. T., “Robot Navigation using a DR/GPS Data Fusion,” Applied Mechanics and Materials, Vol. 392, pp. 261–266, 2013.
Ercan, Z., Sezer, V., Heceoglu, H., Dikilitas, C., Gokasan, M., et al., “Multi-Sensor Data Fusion of DCM based Orientation Estimation for Land Vehicles,” Proc. of IEEE International Conference on Mechatronics (ICM), pp. 672–677, 2011.
Meng, X., Cong, R., and Li, K., “Research on Attributes Discretization in Target Fusion Syetem,” Proc. of World Congress on Information and Communication Technologies (WICT), pp. 1166–1170, 2012.
Yairi, T., Hori, K., and Nakasuka, S., “Sensor Space Discretization in Autonomous Agent based on Entropy Minimization of Behavior Outcomes,” Proc. of IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 111–116, 1999.
Akhoundi, M. A. A. and Valavi, E., “Multi-Sensor Fuzzy Data Fusion using Sensors with Different Characteristics,” The CSI Journal on Computer Science and Engineering, arXiv:1010.6096, 2010.
Joerger, M. and Pervan, B., “Measurement-Level Integration of Carrier-Phase GPS and Laser-Scanner for Outdoor Ground Vehicle Navigation,” Journal of Dynamic Systems, Measurement, and Control, Vol. 131, No. 2, Paper No. 021004, 2009.
Samadzadegan, F. and Abdi, G., “Autonomous Navigation of Unmanned Aerial Vehicles based on Multi-Sensor Data Fusion,” Proc. of 20th Iranian Conference on Electrical Engineering (ICEE), pp. 868–873, 2012.
Li, B., Rizos, C., Lee, H. K., and Lee, H. K., “A Gps-Slaved Time Synchronization System for Hybrid Navigation,” GPS Solutions, Vol. 10, No. 3, pp. 207–217, 2006.
Chiang, K. W. and Huang, Y. W., “An Intelligent Navigator for Seamless INS/GPS Integrated Land Vehicle Navigation Applications,” Applied Soft Computing, Vol. 8, No. 1, pp. 722–733, 2008.
Rodger, J. A., “Toward Reducing Failure Risk in an Integrated Vehicle Health Maintenance System: A Fuzzy Multi-Sensor Data Fusion Kalman Filter Approach for IVHMS,” Expert Systems with Applications, Vol. 39, No. 10, pp. 9821–9836, 2012.
Rigatos, G. G., “Extended Kalman and Particle Filtering for Sensor Fusion In Motion Control of Mobile Robots,” Mathematics and Computers in Simulation, Vol. 81, No. 3, pp. 590–607, 2010.
Qi, W., Zhang, P., and Deng, Z., “Robust Weighted Fusion Time- Varying Kalman Smoothers for Multisensor System with Uncertain Noise Variances,” Information Sciences, Vol. 282, pp. 15–37, 2014.
Julier, S. J. and Uhlmann, J. K., “General Decentralized Data Fusion with Covariance Intersection (CI),” CRC Press, Chap. 12, 2001.
Southall, B., Buxton, B. F., and Marchant, J. A., “Controllability and Observability: Tools for Kalman Filter Design,” Proc. of BMVC, pp. 164–173, 1998.
Kamrani, E., Foroushani, A. N., Vaziripour, M., and Sawan, M., “Detecting the Stable, Observable and Controllable States of the Human Brain Dynamics,” Open Journal of Medical Imaging, Vol. 2, pp. 128, 2012.
Elizabeth, S. and Jothilakshmi, R., “Convergence Analysis of Extended Kalman Filter in a Noisy Environment through Difference Equations,” International Journal of Pure and Applied Mathematics, Vol. 91, No. 1, pp. 33–41, 2014.
Lee, D. J., “Nonlinear Estimation and Multiple Sensor Fusion using Unscented Information Filtering,” IEEE Signal Processing Letters, Vol. 15, pp. 861–864, 2008.
Wendel, J., Meister, O., Schlaile, C., and Trommer, G. F., “An Integrated GPS/MEMS-IMU Navigation System for an Autonomous Helicopter,” Aerospace Science and Technology, Vol. 10, No. 6, pp. 527–533, 2006.
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Vista, F.P., Lee, DJ. & Chong, K.T. Design of an EKF-CI based sensor fusion for robust heading estimation of marine vehicle. Int. J. Precis. Eng. Manuf. 16, 403–407 (2015). https://doi.org/10.1007/s12541-015-0054-9
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DOI: https://doi.org/10.1007/s12541-015-0054-9