  A foremost task in frequency diverse array multiple-input multiple-output
(FDA-MIMO) radar is to efficiently obtain the target signal in the presence of
interferences. In this paper, we employ a novel "low-rank + low-rank + sparse"
decomposition model to extract the low-rank desired signal and suppress the
jamming signals from both barrage and burst jammers. In the literature, the
barrage jamming signals, which are intentionally interfered by enemy jammer
radar, are usually assumed Gaussian distributed. However, such assumption is
oversimplified to hold in practice as the interferences often exhibit
non-Gaussian properties. Those non-Gaussian jamming signals, known as impulsive
noise or burst jamming, are involuntarily deviated from friendly radar or other
working radio equipment including amplifier saturation and sensor failures,
thunderstorms and man-made noise. The estimation performance of the existing
estimators, relied crucially on the Gaussian noise assumption, may degrade
substantially since the probability density function (PDF) of burst jamming has
heavier tails that exceed a few standard deviations than the Gaussian
distribution. To capture a more general signal model with burst jamming in
practice, both barrage jamming and burst jamming are included and a two-step
"Go Decomposition" (GoDec) method via alternating minimization is devised for
such mixed jamming signal model, where the $a$ $priori$ rank information is
exploited to suppress two kinds of jammers and estimate the desired target.
Simulation results verify the robust performance of the devised scheme.
