Statistical Feature Vector (SFV) For SAR ATR

Historically, Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has faced challenges and performance issues because the tendency is to treat SAR imagery the same way we treat optical imagery, whereas SAR complex images are fundamentally a clutter map that only resembles an image. As indicated by the name itself, the Statistical Feature Vector (SFV) is a radar phenomenology-based ATR engine that can map many image processing enhancements to the radar domain, while also including radar unique capabilities that can improves SAR ATR. An SFV is based on information theoretic compression of the SAR target formed from a complex Wiener filter that exploits the holographic nature of SAR images. For example, the Multistage Wiener Filter (MWF) may be used that determines the most compressed scattering space (potentially statistical combinations of scattering center elements) that represent the target. The SFV is shown to be a type of sufficient statistic for SAR ATR where methods of employment are the subject of ongoing research.