Novel Space-Time Adaptive Processing Methods for Gaussian and Non- Gaussian Radar Clutter

摘要

Our work on this contract has four main thrusts. We addressed (1) the problem of optimal target detection of a rank one signal in additive non- Gaussian clutter modeled as a spherically invariant random process. Performance analysis of the optimal signal processor was carried out. However, practical implementation of the optimal processor requires know- ledge of the probability density function underlying the clutter, which is often unavailable. Hence, (2) we considered the performance of sub-optimum as well as ad-hoc approximations to the optimal processor. Next, (3) we concerned ourselves with the performance of parametric space-time adaptive processing methods in Gaussian interference and addressed issues of detection probability. constant false alarm rate and reduced training data support. Finally, (4) we provided a rigorous statistical analysis of the recently proposed non-homogeneity detector, which is useful for training data selection in STAP applications.