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    Partially Constrained Adaptive Beamforming

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    Hornberger_ku_0099M_14214_DATA_1.pdf (1.149Mb)
    Issue Date
    2015-08-31
    Author
    Hornberger, Erik David
    Publisher
    University of Kansas
    Format
    125 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
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    Abstract
    The ReIterative Super-Resolution (RISR) was developed based on an iterative implementation of the Minimum Mean Squared Error (MMSE) estimator. Here, a novel approach to direction of arrival estimation, partially constrained beamforming is introduced by building from existing work on the RISR algorithm. First, RISR is rederived with the addition of a unity gain constraint, with the result denoted as Gain Constrained RISR (GC-RISR), though this formulation exhibits some loss in resolution. However, by taking advantage of the similar structure of RISR and GC-RISR, they can be combined using a geometric weighting term $\alpha$ to form a partially constrained version of RISR, which we denote as PC-RISR. Simulations are used to characterize PC-RISR's performance, where it is shown that the geometric weighting term can be used to control the speed of convergence. It is also demonstrated that this weighting term enables increased super-resolution capability compared to RISR, improves robustness to low sample support for super-resolving signals with low SNR, and the ability to detect signals with an SNR as low as -10dB given higher sample support.
    URI
    http://hdl.handle.net/1808/19399
    Collections
    • Engineering Dissertations and Theses [705]
    • Theses [3710]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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