Type of Document Master's Thesis Author Egger, Sean Robert Author's Email Address firstname.lastname@example.org 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 Keywords
- lossless information fusion
- multi-sensor array
- acoustic simulation
Date of Defense 2009-11-17 Availability unrestricted AbstractNetworked, 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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