Document Type
Thesis
Date of Degree
Spring 2010
Degree Name
MS (Master of Science)
Degree In
Electrical and Computer Engineering
First Advisor
Soura Dasgupta
First Committee Member
Raghu Ram Madumbai
Second Committee Member
Mark Andersland
Abstract
This thesis considers statistical issues in source localization from the received signal strength (RSS) measurements at sensor locations, under the practical assumption of log-normal shadowing. Distance information of source from sensor locations can be estimated from RSS measurements and many algorithms directly use powers of distances to localize the source, even though distance measurements are not directly available. The first part of the thesis considers the statistical analysis of distance estimation from RSS measurments. We show that the underlying problem is inefficient and there is only one unbiased estimator for this problem and its mean square error (MSE) grows exponentially with noise power. Later, we provide the linear minimum mean square error (MMSE) estimator whose bias and MSE are bounded in noise power. The second part of the thesis establishes an isomorphism between estimates of differences between squares of distances and the source location. This is used to completely characterize the class of unbiased estimates of the source location and to show that their MSEs grow exponentially with noise powers. Later, we propose an estimate based on the linear MMSE estimate of distances that has error variance and bias that is bounded in the noise variance.
Keywords
distance estimation, Log normal shadowing, Mean Square Error, Received Signal Strength, Source Localization, Unbiased estimates
Pages
vii, 37 pages
Bibliography
Includes bibliographical references (pages 36-37).
Copyright
Copyright 2010 Sree Divya Chitte