A comparative study of algorithms for radar imaging from gapped data

In ultra wideband (UWB) radar imagery, there are often cases where the radar's operating bandwidth is interrupted due to various reasons, either periodically or randomly. Such interruption produces phase history data gaps, which in turn result in artifacts in the image if conventional image reconstruction techniques are used. The higher level artifacts severely degrade the radar images. In this work, several novel techniques for artifacts suppression in gapped data imaging were discussed. These include: (1) A maximum entropy based gap filling technique using a modified Burg algorithm (MEBGFT); (2) An alternative iteration deconvolution based on minimum entropy (AIDME) and its modified version, a hybrid max-min entropy procedure; (3) A windowed coherent CLEAN algorithm; and (4) Two-dimensional (2-D) periodically-gapped Capon (PG-Capon) and APES (PG-APES) algorithms. Performance of various techniques is comparatively studied.