Sequential hypothesis testing for dynamic SAR resource allocation

In this paper we describe a novel method of automatic target detection applied directly to the synthetic aperture radar (SAR) phase history. Our algorithm is based on a sequential likelihood ratio test (Wald test). The time dynamic behavior of the SAR phase history is modeled as a two dimensional autoregressive (AR) process. The sequential test attempts to dynamically ascertain the presence/absence of a target while the SAR phase history data is being collected. A target/no target decision can then be made during the collection aperture. System resources such as collection aperture and image formation processing can be dynamically reallocated depending on scene content. In contrast, image based detection methods wait until the entire aperture is collected, an image formed, then an algorithm is applied. We will show that significant savings in collection aperture can be obtained using this detection structure which may increase system search rates.