Sparsity-based methods for interrupted radar data reconstruction

Kyle Storm, Vinay Murthy, Ivan Selesnick, Unnikrishna Pillai

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Missing radar data may be reconstructed by using the structure present in surrounding data to make intelligent estimates of values at missing locations. We formulate the interrupted radar data scenario as an ℓ 1- regularized least squares problem, and take advantage of the radar data's demonstrated sparsity in the discrete Fourier domain. Applying the split-variable augmented Lagrangian technique results in an iterative algorithm consisting of two alternating minimizations. The fast algorithm avoids explicit linear inverse solutions, and demonstrates good phase history reconstruction and improved imaging irrespective of the structure of the data loss. Experimental results are presented for synthetic aperture radar (SAR) image formation; however, the approach may also be used with other types of radar data.

Original languageEnglish (US)
Title of host publication2012 IEEE Radar Conference
Subtitle of host publicationUbiquitous Radar, RADARCON 2012 - Conference Program
Number of pages5
StatePublished - 2012
Event2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012 - Atlanta, GA, United States
Duration: May 7 2012May 11 2012

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Other2012 IEEE Radar Conference: Ubiquitous Radar, RADARCON 2012
Country/TerritoryUnited States
CityAtlanta, GA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'Sparsity-based methods for interrupted radar data reconstruction'. Together they form a unique fingerprint.

Cite this