Abstract:
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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. The research
in this area thus far has assumed effective removal of wall elec-
tromagnetic backscatterings prior to CS application. Wall clutter
mitigation can be achieved using full data volume which is, how-
ever, in contradiction with the underlying premise of CS. In this
paper, we enable joint wall clutter mitigation and CS application
using a reduced set of spatial-frequency observations in stepped
frequency radar platforms. Specifically, we demonstrate that wall
mitigation techniques, such as spatial filtering and subspace pro-
jection, can proceed using fewer measurements. We consider both
cases of having the same reduced set of frequencies at each of
the available antenna locations and also when different frequency
measurements are employed at different antenna locations. The
latter casts a more challenging problem, as it is not amenable to
wall removal using direct implementation of filtering or projection
techniques. In this case, we apply CS at each antenna individually
to recover the corresponding range profile and estimate the scene
response at all frequencies. In applying CS, we use prior knowl-
edge of the wall standoff distance to speed up the convergence of
the orthogonal matching pursuit for sparse data reconstruction.
Real data are used for validation of the proposed approach |