A Bayesian perspective on sparse regularization for STAP post-processing

Traditional Space Time Adaptive Processing (STAP) formulations cast the problem as a detection task which results in an optimal decision statistic for a single target in colored Gaussian noise. In the present work, inspired by recent theoretical and algorithmic advances in the field known as compres...

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Bibliographic Details
Published in2010 IEEE Radar Conference pp. 1471 - 1475
Main Authors Parker, Jason T, Potter, Lee C
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Bayesian methods
Casting
Filtering
Force sensors
Interference suppression
Laboratories
Radar clutter
Radar detection
Radar signal processing
Signal processing algorithms
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Summary:Traditional Space Time Adaptive Processing (STAP) formulations cast the problem as a detection task which results in an optimal decision statistic for a single target in colored Gaussian noise. In the present work, inspired by recent theoretical and algorithmic advances in the field known as compressed sensing, we impose a Laplacian prior on the targets themselves which encourages sparsity in the resulting reconstruction of the angle/Doppler plane. By casting the problem in a Bayesian framework, it becomes readily apparent that sparse regularization can be applied as a post-processing step after the use of a traditional STAP algorithm for clutter estimation. Simulation results demonstrate that this approach allows closely spaced targets to be more easily distinguished.
ISBN:9781424458110
1424458110
ISSN:1097-5659
2375-5318
DOI:10.1109/RADAR.2010.5494384