eprintid: 445
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/00/04/45
datestamp: 2011-07-12
lastmod: 2013-07-01 09:04:12
status_changed: 2013-07-01 09:04:12
type: techreport
metadata_visibility: show
item_issues_count: 0
creators_name: Viani, Federico
creators_name: Martinelli, Mauro
creators_name: Ioriatti, Luca
creators_name: Lizzi, Leonardo
creators_name: Oliveri, Giacomo
creators_name: Rocca, Paolo
creators_name: Massa, Andrea
title: Real-Time Indoor Localization and Tracking of Passive Targets by Means of Wireless Sensor Networks
ispublished: pub
subjects: TU
full_text_status: public
note: This version is a pre-print of the final version available at IEEE.
abstract: Recently, the growing need of monitoring private or public areas for security purposes in civilian and military applications is driving the research community to design non-invasive systems based on tiny sensing devices [1]. The tracking of a vehicle in a restricted area, the detection of animals in a dynamic environment, or the analysis of people behavior from movements are few examples of applications where the employment of systems for the localization and tracking of targets is mandatory. In the framework of wireless communications and technologies, the development of low-power and low-cost devices, such as Wireless Sensor Networks (WSN) [2], integrating on-board processing and radio interface has favored the development of efficient cooperative signal processing algorithm for tracking purposes. Most of these systems are based on the processing of data acquired by dedicated sensor, or they assume to localize an active target, namely provided with some transmitting devices [3]. Unfortunately, in many applications the targets can not be equipped with wireless modules and the use of a complex system based on specific sensors is often not affordable. In this work, the localization problem is addressed by considering only the information provided by the quality indexes of the wireless links between the nodes of the WSN as in [4]. Consequently, unlike state-of-the-art approaches, the infrastructure needed by the tracking procedure is limited to the nodes of the WSN, without the need of additional sensors. As a matter of fact, the target moving inside the scenario under test interacts with the electromagnetic signals transmitted by the wireless devices, thus modifying the values of the quality indexes measured at each node of the network. By reformulating such a problem in terms of a simplified electromagnetic inverse scattering problem, the localization and tracking of a passive target is carried out by means of a learning by example (LBE) strategy [5]. With respect to [4], the novelty of this paper lies in the application of the proposed approach to realistic indoor scenarios and in the use of differential measurements in order to remove the background contribution.
date: 2011-01
date_type: published
institution: University of Trento
department: informaticat
refereed: FALSE
referencetext: [1] C.-Y. Chong and S. P. Kumar, “Sensor networks: evolution, opportunities, and challenges,” Proc. IEEE, vol. 91, no. 8, pp. 1247–1256, Aug. 2003. [2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, pp. 393-422, Mar 2002. [3] G. Latsoudas, and N. D. Sidiropoulos, “A fast and effective multidimensional scaling approach for node localization in wireless sensor networks,” IEEE Trans. Geosci. Remote Sens., , vol. 55, no. 10, pp. 5121–5127, Oct. 2007. [4] F. Viani, L. Lizzi, P. Rocca, M. Benedetti, M. Donelli, and A. Massa, “Object Tracking through RSSI Measurements in Wireless Sensor Networks,” Electronic Letters, vol. 44, no. 10, pp. 653-654, May 2008. [5] A. Massa, A. Boni, and M. Donelli, “A classification approach based on SVM for electromagnetic subsurface sensing,” IEEE Trans. Geosci. Remote Sens, vol. 43, no. 9, Sept. 2005. [6] V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998. [7] J. Platt, “Probabilistic outputs for support vector machines and comparison to regularized likelihood methods,” in Smola, A.J., Bartlett, P.,Scholkopf, B. and Schuurmans, D. (Eds.) “Advances in large margin Classifiers”, (MIT Press, Cambridge, MA, 1999).
citation:   Viani, Federico and Martinelli, Mauro and Ioriatti, Luca and Lizzi, Leonardo and Oliveri, Giacomo and Rocca, Paolo and Massa, Andrea  (2011) Real-Time Indoor Localization and Tracking of Passive Targets by Means of Wireless Sensor Networks.  [Technical Report]     
document_url: http://www.eledia.org/students-reports/445/1/DISI-11-192.C175.pdf