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Sensors 2014, 14(12), 23871-23884; doi:10.3390/s141223871

Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks

Department of Mechanical and Aerospace Engineering, Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul KS013, Korea
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Received: 17 October 2014 / Revised: 24 November 2014 / Accepted: 2 December 2014 / Published: 11 December 2014
(This article belongs to the Section Sensor Networks)
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Abstract

Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning, which uses both labeled and unlabeled data obtained from low-cost distributed sensor network. In our setting, labeled data are obtained by seismic and PIR sensors that contain information about the types of the targets. Unlabeled data are generated from the RF signal strength by applying Gaussian process, which represents the probability of predicted target locations. Finally, by using classified unlabeled data produced by semi-supervised learning, identities and locations of multiple targets are estimated. In addition, we consider a case when the labeled data are absent, which can happen due to fault or lack of the deployed sensor nodes and communication failure. We overcome this situation by defining artificial labeled data utilizing characteristics of support vector machine, which provides information on the importance of each training data point. Experimental results demonstrate the accuracy of the proposed tracking algorithm and its robustness to the absence of the labeled data thanks to the artificial labeled data. View Full-Text
Keywords: low-cost sensor network; multi-target tracking; semi-supervised learning; Gaussian process low-cost sensor network; multi-target tracking; semi-supervised learning; Gaussian process
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yoo, J.; Kim, H.J. Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks. Sensors 2014, 14, 23871-23884.

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