Abstract
The miniaturization of sensors has made possible the use of these tiny components in hostile environments for monitoring purposes in the form of sensor networks. Due to the fact that these networks often work in a data centric manner, it is desirable to use machine learning techniques in data aggregation and control. In this paper we give a brief introduction to sensor networks. One of the first attempts to solve the Geolocation problem using Support Vector Regression (SVR) is then discussed. We propose a method to localize a stationary, hostile radar using the Time Difference of Arrival (TDoA) of a characteristic pulse emitted by the radar. Our proposal uses three different Unmanned Aerial Vehicles (UAVs) flying in a fixed triangular formation. The performance of the proposed SVR method is compared with a variation of the Taylor Series Method (TSM) used for solving the same problem and currently deployed by the DSTO, Australia on the Aerosonde Mark III UAVs. We conclude by proposing the application of the SVR approach to more general localization scenarios in Wireless Sensor Networks.
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Palaniswami, M., Sundaram, B., Jayavardhana, R.G., Shilton, A. (2007). Target Localization using Machine Learning. In: Filipe, J., Ferrier, JL., Cetto, J.A., Carvalho, M. (eds) Informatics in Control, Automation and Robotics II. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5626-0_4
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DOI: https://doi.org/10.1007/978-1-4020-5626-0_4
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