Advanced Radar Data Fusion

In Phase I, TSC and the University of Arizona developed a new generalized Space-Time Adaptive Processing (Generalized STAP) algorithm that discriminates among classes of scatterers that move on generalized space-time paths. The new formalism was applied to three-dimensional (3D) imaging of objects in the missile threat complex while suppressing radar dipole clutter by means of adaptive complex weights over the fundamental radar measurement dimensions of time, frequency and element. Demonstrations were given using a single ground-based or sea-based radar and a next-generation coherent radar network. The generalized STAP algorithm is a novel synthesis of ideas from TSC's successful 3D radar imaging programs with adaptive signal processing. In Phase II, an innovative algorithm is offered that adaptively cancels tumbling radar dipole clutter prior to target detection, for subsequent 3D radar imaging. The algorithm is unlike techniques that attempt to `burn through' clutter by using a combination of very large relative bandwidth, ultra-long coherent integration and exhaustive hypothesis testing. Instead, it adaptively produces deep detection nulls in a subspace of the joint image-motion space of ballistic target scatterer motion to facilitate detection, 3D radar imaging and precise motion estimation of targets, while selectively nulling returns from dipoles based on their physical characteristics.