Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

This paper considers a target localization problem whose goal is to estimate the location of an unknown object. It is one of the key issues in applications of wireless sensor networks (WSNs). With recent advances in fabrication technology, deployment of a large WSNs has become economically feasible. On the other hand, this has caused the curse of dimensionality in applying learning algorithms such as support vector regression (SVR). To handle this, we use an ensemble implementation of SVRs for target localization and validate it experimentally. This paper draws a comparison between the conventional SVR method and the proposed method in terms of the accuracy and robustness. Experimental results show that the prediction performance of the proposed method is more accurate and robust to the measurement noise than conventional SVR predictor.