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Sparsity based TomoSAR combining CS and GLRT

Authors: Budillon, Alessandra; Johnsy, Angel Caroline; Schirinzi, Gilda;

Sparsity based TomoSAR combining CS and GLRT

Abstract

In this paper a new approach to TomoSAR imaging is presented. It is based on the joint use of a Constant False Alarm Rate (CFAR) detection approach of multiple targets and of Compressive Sampling (CS) tomographic reconstructions. CS is widely used to recover a sparse signal but suffers from the presence of outliers. The proposed method consists in applying a Generalized Likelihood Ratio Test (GLRT) exploiting the CS reconstruction in order to detect and accurately localize single and double scatterers with a given false alarm probability, avoiding outliers and artefacts.

Country
Italy
Related Organizations
Keywords

Signal Processing; SAR

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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