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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Published in | 2013 IEEE Radar Conference (RadarCon13) pp. 1 - 4 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.04.2013
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Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 9781467357920 1467357928 |
ISSN: | 1097-5659 2375-5318 |
DOI: | 10.1109/RADAR.2013.6586143 |