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  <title><![CDATA[Ph.D. Proposal Oral Exam - Anupama Govinda Raj]]></title>
  <body><![CDATA[<p><strong>Title:&nbsp; </strong><em>Gridless compressed sensing methods for super-resolution direction-of-arrival estimation</em></p>

<p><strong>Committee:&nbsp; </strong></p>

<p>Dr. McClellan, Advisor&nbsp;&nbsp;</p>

<p>Dr. Lanterman, Chair</p>

<p>Dr. Davenport</p>

<p><strong>Abstract: </strong></p>

<p>The objective of the proposed research is to develop&nbsp;gridless&nbsp;super-resolution direction-of-arrival estimation methods for arbitrary array geometries by exploiting sparsity in the continuous angle domain to eliminate the&nbsp;offgrid&nbsp;problem associated with grid-based compressed sensing methods. Making use of the periodicity of the array manifold, the dual function for the infinite-dimensional primal atomic norm minimization problem is represented as a trigonometric polynomial via truncated Fourier series. The dual problem is then converted to a finite semidefinite program, and the source directions are recovered through polynomial rooting. The proposed approach is used to design search-free&nbsp;gridlessmethods applicable for coherent sources, limited snapshots, one-bit sensor measurements, and wideband sources. The performance of the method is evaluated by computer simulations for various array geometries and parameters.</p>
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