Space-time adaptive processing (STAP) with limited sample support

Abstract

A particularly active area of research in space-time adaptive processing (STAP) involves scenarios in which the sample support available for training the adaptive processor is limited. Several of these scenarios are of significant current interest. One of those scenarios is an environment in which targets are potentially so dense (relative to the sample support requirements) that they bias the weight training, thereby causing significant performance degradation of the STAP processor. Such environments include those containing roads and highways, for example. Other related problems include scenarios in which the clutter itself is not homogeneous over significant ranges, e.g. conditions where the terrain type is highly variable, urban environments, etc. One technique that addresses the low-sample support conditions described above is the parametric adaptive matched filter (PAMF). Performance of this technique and several contending. STAP approaches are demonstrated using the KASSPER challenge dataset only.