Abstract:
|
Compressive sensing (CS) for urban operations
and through-the-wall radar imaging has been shown to be
successful in fast data acquisition and moving target
localizations. However, the research work in this area thus far
has assumed prior effective wall removal, allowing proper
detection of indoor targets. In this paper, we show that wall
removal techniques, operating with full data volume and
applying backprojection imaging methods, can be equally
effective under reduced data volume and within the sparse signal
reconstruction framework. Specifically, we demonstrate that the
spatial filtering and the singular value decomposition based
approaches, which, respectively, exploit the spatial invariance
and the strength of the EM wall return, for suppression of the
wall reflections, can be employed using few measurements, thus
allowing CS to be applied to data with higher target-to-wallclutter
ratio. |