Abstract

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 Cramer-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.

Highlights

  • A Self-Localization Method for Wireless Sensor NetworksEach 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

  • Unattended sensor networks are becoming increasingly important in a large number of military and civil applications [1, 2, 3, 4]

  • We develop a maximum likelihood (ML) estimation procedure, and show that it achieves the Cramer-Rao bound (CRB) for reasonable time of arrival (TOA) and direction of arrival (DOA) measurement errors

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Summary

A Self-Localization Method for Wireless Sensor Networks

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 compute the Cramer-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, Cramer-Rao bound

INTRODUCTION
PROBLEM STATEMENT AND NOTATION
EXISTENCE AND UNIQUENESS OF SOLUTIONS
MAXIMUM LIKELIHOOD SELF-CALIBRATION
The maximum likelihood estimate
Nonlinear least squares solution
Estimation accuracy
Partial measurements
NUMERICAL RESULTS
Synthetic data example
Field test results
CONCLUSIONS
Full Text
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