GPU-based space-time adaptive processing (STAP) for radar

Space-time adaptive processing (STAP) utilizes a two-dimensional adaptive filter to detect targets within a radar data set with speeds similar to the background clutter. While adaptively optimal solutions exist, they are prohibitively computationally intensive. Thus, researchers have developed alternative algorithms with nearly optimal filtering performance and greatly reduced computational intensity. While such alternatives reduce the computational requirements, the computational burden remains significant and efficient implementations of such algorithms remains an area of active research. This paper focuses on an efficient graphics processor unit (GPU) based implementation of the extended factored algorithm (EFA) using the compute unified device architecture (CUDA) framework provided by NVIDIA.