SAR/ISAR imagery from gapped data: maximum or minimum entropy?

In UWB synthetic aperture radar (SAR) or inverse SAR (ISAR) imagery, there are several special cases leading to the collections of fragmented (or gapped) data. Both spectrally fragmented waveforms and interrupted SAR data collection result in data gaps, which in turn lead to sidelobe severely corrupted radar images. In these cases, innovative image processing techniques must be developed to recover the degraded images. Different algorithms have proposed in the past. We concentrate on radar imagery from randomly gapped data. Two image processing algorithms based on either maximum or minimum entropy, namely, the Burg algorithm and the alternative iteration deconvolution based on minimum entropy (AIDME) are compared. The performance comparison of the two algorithms suggests a new processing procedure to combine the merits of both techniques for optimal SAR/ISAR imagery from gapped data.