A Self-Localization Method for Wireless Sensor Networks
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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: [email protected]
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: [email protected] 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 Cram´er-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, Cram´er-Rao bound.
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INTRODUCTION
Unattended sensor networks are becoming increasingly important in a large number of military and civil applications [1, 2, 3, 4]. The basic concept is to deploy a large number of low-cost self-powered sensor nodes that acquire and process data. The sensor nodes may include one or more acoustic microphones as well as seismic, magnetic, or imaging sensors. A typical sensor network objective is to detect, track, and classify objects or events in the neighborhood of the network. We consider a sensor deployment architecture as shown in Figure 1. A number of low-cost sensor nodes, each equipped with a processor, a low-power communication transceiver, and one or more sensing capabilities, are set out in a planar region. Each sensor node monitors its environment to detect, track, and characterize signatures. The sensed data is processed locally, and the result is transmitted to a local central information processor (CIP) through a low-power communication network. The CIP fuses sensor information and transmits the processed information to a higher-level processing center.
Higher-level processing center
Sensors
Central information processor
Figure 1: Sensor network architecture. A number of low-cost sensor nodes are deployed in a region. Each sensor node communicates to a local CIP, which relays information to a more distant
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