Abstract
Target localization problem, in which the location of an unknown target is estimated, is one of the key issues in applications of wireless sensor networks (WSNs). Target localization methods that directly use raw sensor data can suffer from uncertainty or disturbance caused by the surrounding environmental elements and noise. Especially, when using WSNs, the limited communication capacity can impose a significant limit on the amount of data that can be processed. Considering these issues, various methods have been proposed, especially using machine learning techniques such as neural networks or support vector machines. In this paper, we employ a multi-class classification algorithm for target localization, in which a pseudo probability map is constructed using modified support vector domain description. A local classification strategy which uses information from local neighbors only is proposed in order to reduce communication costs. Experimental results using an acoustic WSN are compared with Platt’s method to validate the multi-class classification algorithm.
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Woojin Kim received his B.S. degree in electrical engineering from Korea Advanced Institute of Technology, Daejeon, South Korea, in 2008. He is currently pursuing a Ph.D. degree in the School of Mechanical and Aerospace Engineering at the Seoul National University, Seoul, Korea. His research interests are distributed learning in networked control systems, data fusion for cooperative control of multiple mobile robots, and applications of sensor networks.
Jaemann Park received the B.S. and M.S. degrees in mechanical engineering from Seoul National University, Seoul, South Korea, in 2008 and 2010, where he is currently pursuing the Ph.D. degree. His research interests include cooperative control of multi-agent systems, nonlinear optimization for aerospace platforms, nonlinear control of industrial manipulators, and also applications of learning systems such as recurrent neural networks.
H. Jin Kim received her B.S. degree in mechanical engineering from Korean Advanced Institute of Technology in 1995, and M.S. and Ph.D. degrees from the University of California, Berkeley, USA, in 1999 and 2001, respectively. From 2002 to 2004, she was a postdoctoral researcher in Electrical Engineering and Computer Sciences at University of California, Berkeley. In 2004, she joined the School of Mechanical and Aerospace Engineering at Seoul National University, where she is currently an Associate Professor. Her research interests are mobile robots and wireless sensor networks.
Chan Gook Park received his B.S., M.S., and Ph.D. degrees in Control and Instrumentation Engineering at Seoul National University in 1985, 1987, and 1993, respectively. He was a Professor in Information and Control Engineering at Kwangwoon University from 1994 and 2002, before joining the School of Mechanical and Aerospace Engineering at Seoul National University in 2002, where he is currently a Professor. His research interests are estimation and filtering algorithms, with applications toward personal navigation systems and faulttolerant control of aerospace vehicles.
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Kim, W., Park, J., Kim, H.J. et al. A multi-class classification approach for target localization in wireless sensor networks. J Mech Sci Technol 28, 323–329 (2014). https://doi.org/10.1007/s12206-013-0969-y
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DOI: https://doi.org/10.1007/s12206-013-0969-y