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Unformatted text preview: Robust and Low Complexity Source Localization in Wireless Sensor Networks Using Time Difference of Arrival Measurement Enyang Xu 1 , Zhi Ding 1 and Soura Dasgupta 2 1 Department of Electrical and Computer Engineering, University of California Davis, CA 95616 2 Department of Electrical and Computer Engineering, University of Iowa, Iowa, IA 52242. Email: eyxu@ece.ucdavis.edu, zding@ece.ucdavis.edu, dasgupta@engineering.uiowa.edu. Abstract Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate robust and low complexity solutions to the problem of source localization based on the timedifference of arrivals (TDOA) measurement model. By adopting a minmax approxi mation to the maximum likelihood source location estimation, we develop two low complexity algorithms that can be reliably and rapidly solved through semidefinite relaxation. Our approach hinges on the use of a reference sensor node which can be optimized according to the Cram e rRao lower bound or selected heuristically. Our low complexity estimate can be used either as the final location estimation output or as the initial point for other traditional search algorithms. I. INTRODUCTION With the rapidly expanding interests and applications of wireless sensor networks, the problem of robust localization has become increasingly important [1]. Wireless source local ization has broad applications in target tracking, signal routing, interference cancellation, and emergency response, among oth ers. Source localization typically involves estimating positions of signal emitters in a network of sensors that measure the source signal information. A data fusion center generates the source location estimate by taking into account the collective signal measurement from the sensors. In practice, the various data fusion methods include time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), angle of arrival (AOA), and various combinations of these [2]. Similarly, an equivalent problem of sensor localization involves the estimation by the sensor of its own location based on the reception of multiple source signals. The problem of source localization has been studied in a number of published works, e.g, [1], [2], based on var ious measurement models. The authors of [3] presented a semidefinite programming (SDP) algorithm for a noisy dis tance measurement model by minimizing the l 1 estimation error. This proposed framework can also integrate additional angleofarrival information. By applying a received signal strength measurement under the well known lognormal fad ing model, the authors of [4] also derived efficient SDP algorithms for source localization based on the minimax criteria. However, in many applications, direct distance and signal strength measurements may not be directly available for source estimation. Additionally, in wireless environment with rich scatters, signal strength measurement can be highly...
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 Spring '10
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