A Self-Localization Method for Wireless Sensor Networks.pdf

EURASIP Journal on Applied Signal Processing 2003:4, 348–358 c? 2003 Hindawi Publishing Corporation A Self-Localization Method for Wireless Sensor Networks Randolph L. Moses Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA Email: moses.2@osu.edu Dushyanth Krishnamurthy Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA Robert M. Patterson Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA Email: Robert.M.Patterson@jhuapl.edu Received 30 November 2001 and in revised form 9 October 2002 We consider the problem of locating and orienting a network of unattended sensor nodes that have been deployed in a scene at unknown locations and orientation angles. This self-calibration problem is solved by placing a number of source signals, also with unknown locations, in the scene. Each source in turn emits a calibration signal, and a subset of sensor nodes in the network measures the time of arrival and direction of arrival (with respect to the sensor node’s local orientation coordinates) of the signal emitted from that source. From these measurements we compute the sensor node locations and orientations, along with any unknown source locations and emission times. We develop necessary conditions for solving the self-calibration problem and provide a maximum likelihood solution and corresponding location error estimate. We also compute the Crame?r-Rao bound of the sensor node location and orientation estimates, which provides a lower bound on calibration accuracy. Results using both synthetic data and field measurements are presented. Keywords and phrases: sensor networks, localization, location uncertainty, Crame?r-Rao bound. 1. INTRODUCTION Unattended sensor networks are becoming increasingly im- portant in a large number of military and civil applications [1, 2, 3, 4]. The basic concept is to deploy a large nu