UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Dillon, MatthewSubject: search.filters.subject.radio

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    Distributed Passive Sensor Network for the Geolocation of RF Emitters
    (2019) Dillon, Matthew; Ephremides, Anthony; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The ability to localize an RF emitter has emerged in both commercial and military technology, and is an important capability in modern cognitive radios to achieve spectral awareness. Of importance, is the accuracy of the geolocation of the RF emitter. In this thesis, we address the blind localization problem given a network of software-defined radio receivers that monitor the spectrum to determine the presence of an unknown emitter. We discuss the underlying challenges and various approaches to the geolocation problem that can be utilized. In particular, two algorithms that are used extensively in literature are investigated: time-difference of arrival, and power-difference of arrival. In the first part of the thesis, the algorithms are presented, and the error performance is characterized analytically, and then conducted through simulation. A more robust method which implements the fusion of both algorithms for an improved estimation. In the second part, we conduct a small- scale laboratory emulation of the geolocation algorithms on a network of radios to contrast the simulation results of the algorithms from the emulation results. The results provided insight to the shortcomings of each algorithm, and potential extensions for further accuracy improvement.