  Common ISAR radar images and signals can be reconstructed from much fewer
samples than the sampling theorem requires since they are usually sparse.
Unavailable randomly positioned samples can result from heavily corrupted parts
of the signal. Since these samples can be omitted and declared as unavailable,
the application of the compressive sensing methods in the recovery of heavily
corrupted signal and radar images is possible. A\ simple direct method for the
recovery of unavailable signal samples and the calculation of the restored ISAR
image is reviewed. An analysis of the noise influence is performed. For fast
maneuvering ISAR targets the sparsity property is lost since the ISAR image is
blurred. A nonparametric quadratic time-frequency representations based method
is used to restore the ISAR image sparsity. However, the linear relation
between the signal and the sparsity domain transformation is lost. A recently
proposed gradient recovery algorithm is adapted for this kind of analysis. It
does not require the linear relation of the signal and its sparsity domain
transformation in the process of unavailable data recovery. The presented
methods and results are tested on several numerical examples proving the
expected accuracy and improvements.
