Derivations of Probability Density Functions for Indoor Position Estimation

Show simple item record Mohd Azmi, KH en 2017-07-02T23:13:28Z en 2017-06-21 en
dc.identifier.citation 21 Jun 2017. Update 30 Sep 2017. 28 pages en
dc.identifier.uri en
dc.description.abstract This report presents a detailed theoretical approach in developing the es- timator equations and its probability density functions (pdfs) in estimating distance and position of an unknown node in an indoor environment via the received signal strength (RSS) method. The channel parameter equations derived via the maximum likelihood estimation method are presented, in which the information is later used to derive the distance estimator equation and its pdf. It is shown in the report that the pdf of the distance estimator follows a lognormal distribution. The estimator equations for the Cartesian position of the unknown node are also derived and it is shown that both of the position estimator equations are a summation of 3 lognormal terms. Using the Cen- tral Limit Theorem, we have approximated the pdf of the position estimator as Gaussian. Finally, it can be shown in the report that the pdf of radius of positioning error can be approximated as having either Rayleigh distribution or Nakagami-m distribution. However, an empirical investigation must be carried out to determine which distribution provides the best t of the pdf of positioning error. en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.title Derivations of Probability Density Functions for Indoor Position Estimation en
dc.type Report en
dc.rights.accessrights en
pubs.subtype Technical Report en
pubs.elements-id 633606 en
pubs.record-created-at-source-date 2017-06-30 en

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