Compressive Sensing for radar STAP

We present a technique that merges Compressive Sensing and Wiener filtering (denoted as CS-Wiener) and compares it to standard Wiener filtering (denoted as Std-Wiener) in radar detection and estimation in a Space-Time Adaptive Processing application. The method leverages an inherent similarity in th...

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Bibliographic Details
Published in2013 IEEE Radar Conference (RadarCon13) pp. 1 - 4
Main Authors Picciolo, Michael L., Goldstein, J. Scott, Myrick, Wilbur L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2013
Subjects
Compressed sensing
Interference
Jamming
Radar
Signal to noise ratio
Vectors
Wiener filters
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Summary:We present a technique that merges Compressive Sensing and Wiener filtering (denoted as CS-Wiener) and compares it to standard Wiener filtering (denoted as Std-Wiener) in radar detection and estimation in a Space-Time Adaptive Processing application. The method leverages an inherent similarity in the Generalized Sidelobe Canceller transformation to that of the Compressive Sensing sampling function, namely its broadband, random or `white' characteristic. We illustrate a data sampling reduction factor of 84% by using a CS-Wiener algorithm as compared to Std-Wiener approaches, while achieving approximately identical performance in SINR and subsequent radar detection performance.
ISBN:9781467357920
1467357928
ISSN:1097-5659
2375-5318
DOI:10.1109/RADAR.2013.6586143