Sparsity and compressed sensing in radar imaging

Potter, Lee C. and Ertin, Emre and Parker, Jason T. and Çetin, Müjdat (2009) Sparsity and compressed sensing in radar imaging. (Accepted/In Press)

WarningThere is a more recent version of this item available.

[img]PDF - Repository staff only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the unknown scene and offer a means for regularizing the inversion task. The emerging field of compressed sensing combines nonlinear reconstruction algorithms and pseudorandom linear measurements to provide reconstruction guarantees for sparse solutions to linear inverse problems. This review article surveys the use of sparse reconstruction algorithms and randomized measurement strategies in radar processing. Although the two themes have a long history in radar literature, the accessible framework provided by compressed sensing illuminates the impact of joining these themes. Potential future directions are conjectured both for extension of theory motivated by practice and for modification of practice based on theoretical insights.

Item Type:Article
Uncontrolled Keywords:Sparse reconstruction, radar ambiguity function, synthetic aperture radar, moving target indication, random arrays, penalized least-squares
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:13028
Deposited By:Müjdat Çetin
Deposited On:04 Dec 2009 10:52
Last Modified:22 Jul 2019 16:06

Available Versions of this Item

Repository Staff Only: item control page