Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-020711-142357


Document Typethesis
Author NameCavanaugh, Andrew F
URNetd-020711-142357
TitleBayesian Information Fusion for Precision Indoor Location
DegreeMS
DepartmentElectrical & Computer Engineering
Advisors
  • David Cyganski, Advisor
  • R. James Duckworth, Co-Advisor
  • John Orr, Committee Member
  • Keywords
  • signal processing
  • location
  • tracking
  • bayesian statistics
  • Date of Presentation/Defense2011-01-19
    Availability unrestricted

    Abstract

    This thesis documents work which is part of the ongoing effort by the Worcester Polytechnic Institute (WPI) Precision Personnel Locator (PPL) project, to track and locate first responders in urban/indoor settings. Specifically, the project intends to produce a system which can accurately determine the floor that a person is on, as well as where on the floor that person is, with sub-meter accuracy. The system must be portable, rugged, fast to set up, and require no pre-installed infrastructure. Several recent advances have enabled us to get closer to meeting these goals: The development of Transactional Array Reconciliation Tomography(TART) algorithm, and corresponding locator hardware, as well as the integration of barometric sensors, and a new antenna deployment scheme. To fully utilize these new capabilities, a Bayesian Fusion algorithm has been designed. The goal of this thesis is to present the necessary methods for incorporating diverse sources of information, in a constructive manner, to improve the performance of the PPL system. While the conceptual methods presented within are meant to be general, the experimental results will focus on the fusion of barometric height estimates and RF data. These information sources will be processed with our existing Singular Value Array Reconciliation Tomography (σART), and the new TART algorithm, using a Bayesian Fusion algorithm to more accurately estimate indoor locations.

    Files
  • cavanaugh_ms.pdf

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