Adaptive CFAR detection of range-spread targets based on SAR raw data

Conventionally, SAR automatic target detection (ATD ) are often performed in SAR image domain. A novel scheme of SAR target detection in the state of non-imaging is presented. More precisely, this paper addresses adaptive detection of SAR possibly extended targets when implemented on range-compressed but azimuth-uncompressed SAR raw data, rather than the processed SAR image. The SAR target detection is established in the context of space-time adaptive processing (STAR) and the spatial-temporal steering vector of an airborne stripmap SAR is first derived by exploiting signature diversity, namely of the fact that SAR can change the transmitted signal as the azimuth varies. The generalized adaptive subspace detector (GASD) is employed to detect range-spread target from SAR raw data. Performance analysis of the proposed scheme is carried out via Monte Carlo simulation which shows the validity of the new scheme in possible realistic scenarios.