Abstract
In this paper, the localization of persons by means of a Wireless Sensor Network (WSN) is considered. Persons carry on-body sensor nodes and move within a WSN. The location of each person is calculated on this node and communicated through the network to a central data sink for visualization. Applications of such a system could be found in mass casualty events, firefighter scenarios, hospitals or retirement homes for example.
For the location estimation on the sensor node, three derivatives of the Kalman filter and a closed-form solution (CFS) are applied, compared, and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN is implemented and data are collected in in- and outdoor environments with differently positioned on-body nodes. The described estimators are then evaluated off-line on the experimentally collected data.
The goal of this paper is to present a comprehensive real-world evaluation of methods for person localization in a WSN based on received signal strength (RSS) range measurements. It is concluded that person localization in in- and outdoor environments is possible under the considered conditions with the considered filters. The compared methods allow for sufficiently accurate localization results and are robust against inaccurate range measurements.
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References
Akyildiz, I., Melodia, T., Chowdhury, K.: A Survey on Wireless Multimedia Sensor Networks. Computer Networks 51(4), 921–960 (2007)
Bahl, P., Padmanabhan, V.: RADAR: An In-Building RF-Based User Location and Tracking System. In: IEEE INFOCOM, vol. 2, pp. 775–784 (2000)
Beutler, F., Huber, M.F., Hanebeck, U.D.: Optimal Stochastic Linearization for Range-based Localization. In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan (2010)
Depenthal, C., Schwendemann, J.: iGPS–a New System for Static and Kinematic Measurements. In: 9th Conference on Optical 3D Measurement Techniques (2009)
Fox, D., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian Filtering for Location Estimation. IEEE Pervasive Computing, 24–33 (2003)
Guvenc?, I., Abdallah, C., Jordan, R., Dedeoglu, O.: Enhancements to RSS Based Indoor Tracking Systems using Kalman Filters. In: GSPx & International Signal Processing Conference (2003)
Hanebeck, U.D., Schmidt, G.: Closed–Form Elliptic Location with an Arbitrary Array Topology. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Atlanta, Georgia, pp. 3070–3073 (1996)
Jimenez, A., Seco, F., Prieto, J., Guevara, J.: Indoor Pedestrian Navigation using an INS/EKF Framework for Yaw Drift Reduction and a Foot-mounted IMU. In: Proceedings of the 7th Workshop on Positioning, Navigation and Communication, Dresden, Germany, March 11-12 (2010)
Julier, S.J., Uhlmann, J.K.: A New Extension of the Kalman Filter to Nonlinear Systems. In: Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, Orlando (1997)
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME - Journal of Basic Engineering, 35–45 (1960)
Lorincz, K., Welsh, M.: MoteTrack: a Robust, Decentralized Approach to RF-based Location Tracking. Personal and Ubiquitous Computing 11(6), 489–503 (2007)
Madigan, D., Einahrawy, E., Martin, R., Ju, W., Krishnan, P., Krishnakumar, A.: Bayesian Indoor Positioning Systems. In: 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Proceedings IEEE INFOCOM 2005, pp. 1217–1227 (2005)
Mao, G., Anderson, B., Fidan, B.: Path Loss Exponent Estimation for Wireless Sensor Network Localization. Computer Networks 51(10), 2467–2483 (2007)
Morelli, C., Nicoli, M., Rampa, V., Spagnolini, U., Alippi, C.: Particle Filters for RSS-based Localization in Wireless Sensor Networks: An Experimental Study. In: 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 Proceedings, vol. 4. IEEE, Los Alamitos (2006)
Mutambara, A.G.O.: Decentralized Estimation and Control for Multisensor Systems. CRC Press, Inc., Boca Raton (1998)
Noh, A., Lee, W., Ye, J.: Comparison of the Mechanisms of the Zigbee’s Indoor Localization Algorithm. In: Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2008), pp. 13–18. IEEE, Los Alamitos (2008)
Pathirana, P., Bulusu, N., Savkin, A., Jha, S.: Node Localization using Mobile Robots in Delay-Tolerant Sensor Networks. IEEE Transactions on Mobile Computing, 285–296 (2005)
Paul, A., Wan, E.: RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers. Journal of IEEE Selected Topics in Signal Processing 3(5), 860–873 (2009)
Rappaport, T., et al.: Wireless Communications: Principles and Practice. Prentice Hall PTR, Upper Saddle River (2002)
Rudafshani, M., Datta, S.: Localization in Wireless Sensor Networks. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, p. 60. ACM, New York (2007)
Seco, F., Jiménez, A., Prieto, C., Roa, J., Koutsou, K.: A Survey of Mathematical Methods for Indoor Localization. In: IEEE International Symposium on Intelligent Signal Processing (WISP), pp. 9–14 (2009)
Smith, J., Abel, J.: Closed-Form Least-Squares Source Location Estimation from Range-Difference Measurements. Proceedings of the 1987 IEEE Transactions on Acoustics, Speech and Signal Processing (ICASSP) 35(12), 1661–1669 (1987)
Sugano, M., Kawazoe, T., Ohta, Y., Murata, M.: Indoor Localization System using RSSI Measurement of Wireless Sensor Network based on Zigbee Standard. In: Proc. IASTED Int. Conf. WSN, pp. 1–6 (2006)
Whitehouse, K., Karlof, C., Culler, D.: A Practical Evaluation of Radio Signal Strength for Ranging-Based Localization. ACM SIGMOBILE Mobile Computing and Communications Review 11(1), 52 (2007)
Xiao, Z., Hei, Y., Yu, Q., Yi, K.: A Survey on Impulse-Radio UWB Localization. Science China Information Sciences 53(7), 1322–1335 (2010)
Yang, S., Cha, H.-j.: An empirical study of antenna characteristics toward RF-based localization for IEEE 802.15.4 sensor nodes. In: Langendoen, K.G., Voigt, T. (eds.) EWSN 2007. LNCS, vol. 4373, pp. 309–324. Springer, Heidelberg (2007)
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Schmid, J., Beutler, F., Noack, B., Hanebeck, U.D., Müller-Glaser, K.D. (2011). An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks. In: Marrón, P.J., Whitehouse, K. (eds) Wireless Sensor Networks. EWSN 2011. Lecture Notes in Computer Science, vol 6567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19186-2_10
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DOI: https://doi.org/10.1007/978-3-642-19186-2_10
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