Abstract

The methods employed in time-reversal imaging are applied to radar imaging problems using multistatic data collected from sparse and unstructured phased array antenna systems. The theory is especially suitable to problems involving the detection and tracking (locating) of moving ground targets (MGT) from satellite based phased array antenna systems and locating buried or obscured targets from multistatic data collected from phased array antenna systems mounted on unmanned aerial vehicles (UAV). The theory is based on the singular value decomposition (SVD) of the multistatic data matrix $K$ and applies to general phased array antenna systems whose elements are arbitrarily located in space. It is shown that the singular vectors of the $K$ matrix together with knowledge of the Green function of the background medium in which the targets are embedded lead directly to classical time-reversal based images of the target locations as well as super-resolution images based on a generalized Multiple-Signal-Classification algorithm recently developed for use with the $K$ matrix. The theory is applied in a computer simulation study of the TechSat project whose goal is the location of MGTs from an unstructured and sparse phased array of freely orbiting antennas located above the ionosphere.