Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

Target localization, whose goal is to estimate the location of an unknown target, is one of the key issues in applications of wireless sensor networks (WSNs). With recent advances in fabrication technology, deployments of large-scale WSNs have become economically feasible. However, there exist issue...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2013-08, Vol.43 (4), p.1189-1198
Main Authors: Woojin Kim, Jaemann Park, Jaehyun Yoo, Kim, H. Jin, Chan Gook Park
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Convergence
Ensemble support vector regression (SVR)
Localization
Mathematical analysis
Noise measurement
Position (location)
Prediction algorithms
Regression
Remote sensors
Robot sensing systems
Robustness
Studies
target localization
Vectors (mathematics)
Wireless networks
Wireless sensor networks
wireless sensor networks (WSNs)
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Summary:Target localization, whose goal is to estimate the location of an unknown target, is one of the key issues in applications of wireless sensor networks (WSNs). With recent advances in fabrication technology, deployments of large-scale WSNs have become economically feasible. However, there exist issues such as limited communication and the curse of dimensionality in applying machine-learning algorithms such as support vector regression (SVR) on large-scale WSNs. Here, in order to overcome such issues, we propose an ensemble implementation of SVR for the problem of target localization. The convergence property of the localization algorithm using the ensemble SVR is verified, and the robustness of the proposed scheme against measurement noise is analyzed. Furthermore, experimental results confirm that the estimation performance of the proposed method is more accurate and robust to measurement noise than the conventional SVR predictor.
ISSN:2168-2267
2168-2275
DOI:10.1109/TSMCB.2012.2226151