Training Strategy for Doppler and Spatial Ambiguous Nonhomogenous Clutter

Abstract:
In this paper, the problem of adaptive weight training in STAP for the scenario of Doppler and Spatially ambiguous, non-homogenous clutter environment is addressed. A conventional method to tackle high false alarms caused by spiky clutter is to include only the returns with the largest power in training. The disadvantage of this method is the possibility of target self nulling which might degrade the performance of clutter mitigation. To overcome this problem, the phase data for each snapshot might be exploited to differentiate between strong clutter and target signals. One method is using interferometric measurement of phase to excise targets from the training data. However, this method might not be sufficient for Doppler and Spatially ambiguous clutter. A proposed method to handle such a scenario is presented.