A Positioning Scheme Combining Location Tracking with Vision Assisting for Wireless Sensor Networks

 

F. Tsai¹, Y.-S. Chiou*² , H. Chang³

 

^1,2 Center for Space and Remote Sensing Research, National Central University Jhongli, Taoyuan, Taiwan.

² Department of Electronic Engineering, Chung Yuan Christian University Jhongli, Taoyuan, Taiwan, *choice@alumni.ncu.edu.tw.

^1,3 Department of Civil Engineering, National Central University Jhongli, Taoyuan, Taiwan.

 

ABSTRACT

This paper presents the performance of an adaptive location-estimation technique combining Kalman filtering (KF) with vision assisting for wireless sensor networks. For improving the accuracy of a location estimator, a KF procedure is employed at a mobile terminal to filter variations of the location estimate. Furthermore, using a vision-assisted calibration technique, the proposed approach based on the normalized cross-correlation scheme is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in real dynamic environments. Namely, according to the vision-assisted approach to extract the locations of the reference nodes as landmarks, a KF-based approach with the landmark information can calibrate the location estimation and reduce the corner effect of a location-estimation system. In terms of the location accuracy estimated from the proposed approach, the experimental results demonstrate that more than 60 percent of the location estimates have error distances less than 1.4 meters in a ZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, the proposed algorithm can achieve reasonably good performance.

Keywords: Kalman filtering, location estimation and tracking, normalized cross correlation, wireless sensor network, zigBee positioning system.

 

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Acknowledgements

This work was supported in part by the National Science Council of the Republic of China (R.O.C.) under Grants NSC 98-2221-E-008-097-MY2 and NSC 101-2218-E-033-007.

 

References

[1] P. Bellavista et al., "Location-Based Services: Back to The Future", IEEE Pervasive Computing, vol. 7, no. 2, pp. 85-89, 2008.         [ Links ]

[2] H.-J. Kwak and G.-T. Park, "Study on The Mobility of Service Robots", International Journal of Engineering and Technology Innovation, vol. 2, no. 2, pp. 13-28, 2012.         [ Links ]

[3] L. Villaseñor et al., "Mean Receiver Power Prediction For Indoors 802.11 WLANs Using The Ray Tracing Technique", Journal of Applied Research and Technology, vol. 5, no. 1, pp. 33-48, Apr. 2007.         [ Links ]

[4] G. Wang et al., "Adaptive Location Updates For Mobile Sinks In Wireless Sensor Networks", The Journal of Supercomputing, vol. 47, issue 2, pp. 127-145, Feb., 2009.         [ Links ]

[5] D. Munoz-Rodriguez et al., "Maximum Likelihood Position Location with a Limited Number of References", Journal of Applied Research and Technology, vol 9, no 1, pp. 5-18, Apr. 2011.         [ Links ]

[6] T. K. Moon and W. C. Stirling, "Mathematical Methods and Algorithms for Signal Processing". Prentice Hall, New Jersey, 2000.         [ Links ]

[7] Y.-S. Chiou et al., "An Adaptive Location Estimator Using Tracking Algorithms for Indoor WLANs", ACM Wireless Networks, vol. 16, no. 7, pp. 1987-2012, 2010.         [ Links ]

[8] G. Glanzer et al., "Semi-Autonomous Indoor Positioning Using MEMS-Based Inertial Measurement Units and Building Information", Proceedings of The IEEE Workshop on Positioning, Navigation and Communication, Mar. 2009, pp. 135-139.         [ Links ]

[9] C. Fischer and H, Gellersen "Location and Navigation Support for Emergency Responders: A Survey", IEEE Pervasive Computing, vol. 9, no. 1, pp. 38-47, 2010.         [ Links ]

[10] O. Cappé et al., "An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo", Proc. IEEE, vol. 95, no. 5, pp. 899-924., 2007        [ Links ]

[11] Y.-S. Chiou et al., "A Reduced-Complexity Scheme Using Message Passing for Location Tracking", EURASIP J. Adv. Signal Process., vol. 2012, 2012.         [ Links ]

[12] M.A. Caceres et al., "Adaptive Location Tracking by Kalman Filter in Wireless Sensor Networks", Proceedings of The IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Oct. 2009, pp. 123-128.         [ Links ]

[13] Y.-S. Chiou et al., "A Low-Complexity Data-Fusion Algorithm Based on Adaptive Weighting for Location Estimation", Proceedings of The IEEE International Conference on Information Security and Intelligent Control (ISIC), Yunlin, Taiwan, Aug. 2012, pp. 296-299.         [ Links ]

[14] A. Hauschild and O. Montenbruck, "Kalman-Filter-Based GPS Clock Estimation for Near Real-Time Positioning", GPS Solutions, vol. 13, issue 3, pp 173-182, Jul. 2009.         [ Links ]

[15] P. Zheng and L. M. Ni, "The Rise of The Smart Phone", IEEE Distributed Systems Online, vol. 7, no. 3, 2006, art. no. 0603-o3003.         [ Links ]

[16] P. R. Wolf and B. A. Dewitt, "Elements of Photogrammetry with Applications in GIS", McGraw-Hill, Taipei, 2000.         [ Links ]

[17] CC2431 Location Engine, Available: http://www.ti.com/product/cc2431.         [ Links ]

[18] C.-L. Wang et al., "An Indoor Location Scheme Based on Wireless Local Area Networks", Proceedings of The IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, Jan. 2005, pp. 602-604.         [ Links ]