Paper Info
Title
Sparsity and Compressed Sensing in Radar Imaging
Abstract
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 paper 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.
Year
DOI
Venue
2010
10.1109/JPROC.2009.2037526
Proceedings of the IEEE
Keywords
Field
DocType
measurement systems,radar imaging,remote sensing by radar,compressed sensing,ill-posed linear inverse problem,nonlinear reconstruction algorithms,pseudorandom linear measurements,radar imaging,radar processing,randomized measurement strategies,remote sensing,scattered electric fields,sparse reconstruction algorithms,Moving target indication,penalized least squares,radar ambiguity function,random arrays,sparse reconstruction,synthetic aperture radar
Ambiguity function,Radar,Radar imaging,Moving target indication,Synthetic aperture radar,Computer science,Algorithm,Electronic engineering,Inverse problem,Compressed sensing,Pseudorandom number generator
Journal
Volume
Issue
ISSN
98
6
0018-9219
Citations 
PageRank 
References 
159
5.89
30
Authors
4
Search Limit
100159
Name
Order
Citations
PageRank
Lee C. Potter144935.60
Emre Ertin21595.89
Jason T. Parker31928.11
Müjdat Çetin41342112.26