Title page for ETD etd-11302009-123833

Type of Document Master's Thesis
Author Egger, Sean Robert
Author's Email Address sregger@vt.edu
URN etd-11302009-123833
Title A Turbo Approach to Distributed Acoustic Detection and Estimation
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Roan, Michael J. Committee Chair
Hong, Dennis W. Committee Member
Johnson, Martin E. Committee Member
  • lossless information fusion
  • multi-sensor array
  • acoustic simulation
Date of Defense 2009-11-17
Availability unrestricted
Networked, multi-sensor array systems have proven to be advantageous in the

sensor world. A large amount of research has been conducted with these systems, with a

main interest in data fusion. Intelligently processing the large amounts of data collected

by these systems is required in order to fully utilize the benefits of a multi-sensor array

system. A robust but flexible simulation environment would provide a platform for

accurately comparing current and future data fusion theories.

This thesis proposes a simulator model for testing fusion theories for these acoustic

multi-sensor networks. An iterative, lossless data fusion algorithm was presented as the

model for simulation development. The arrangement and orientation of objects in the

simulation environment, as well as most other system parameters are defined by the user

before the simulation runs. The sensor data, including noise, is generated at the

appropriate time delay and propagation loss before being processed by a delay and sum

beamformer and a matched filter. The resulting range-Doppler maps are modified to

probability density functions, and translated to a single point of reference. The data is

then combined into a single world model.

An iterative process is used to filter out false targets and amplify true target

detections. Data is fused from each multi-sensor array and from each simulation run.

Target amplitudes are gained if they are present in all combined world models, and are

otherwise reduced. This thesis presents the results of the fusion algorithm used, including

multiple iterations, to prove the algorithms effectiveness.

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