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    Estimation of velocity in underwater wireless channels

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    BLANKENAGEL-THESIS-2013.pdf (2.611Mb)
    Date
    2013-11-25
    Author
    Blankenagel, Bryan
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    Abstract
    Underwater communication is necessary for a variety of applications, including transmission of diver speech, communication between manned and/or unmanned underwater vehicles, and data harvesting for environmental monitoring, to name a few. Examples of communication between underwater vehicles include unmanned or autonomous underwater vehicles (UUV or AUV) for deep water construction, military UUVs such as submarine drones, repair vehicles for deep water oil wells, scientific or resource exploration, etc. Examples of underwater communication between fixed submerged devices are sensor networks deployed on the ocean floor for seismic monitoring and tsunami prediction, pollution monitoring, tactical surveillance, analysis of resource deposits, oceanographic studies, etc. The underwater communication environment is a challenging one. Radio signals experience drastic attenuation, while optical signals suffer from dispersion. Because of these issues, acoustic (sound) signals are usually used for underwater communication. Unfortunately, acoustics has its own problems, including limited bandwidth, slow propagation, and signal distortion. Some of these limitations can be overcome with advanced modulation and coding, but to do so requires better understanding of the underwater acoustic propagation environment, which is significantly different than air- or space-based radio propagation. The underwater environment must be studied and characterized to exploit these advanced modulation and coding techniques. This thesis addresses some of these concerns by proposing a derivation of the envelope level crossing rate of the underwater channel, as well as a simulation model for the channel, both of which agree well with the measured results. A velocity estimator is also proposed, but suffers from a high degree of root mean square error
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    http://hdl.handle.net/1853/50418
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    • Georgia Tech Theses and Dissertations [22398]
    • School of Electrical and Computer Engineering Theses and Dissertations [3127]

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