Moving Target Detection and Characterization with circular SAR

In this work, we examine ground target detection and characterization from radar data, which incorporates a model-based optimization method called dynamic logic (DL). We apply our methodology to a prototype airborne radar platform called Gotcha, developed by the Air Force Research Laboratory/Sensors Directorate/Automatic Target Recognition Division in recent years. The aircraft traces out a circular path around an area of interest, and the onboard, side-looking radar transmits and receives energy at a constant pulse repetition frequency, while the main beam direction is maintained at a fixed aim point on the ground. Data collected during any appropriate length arc of the flight path can be used to create synthetic aperture radar (SAR) images of the ground. The data can also be used for ground moving target indication (GMTI) and provide Doppler/Range imagery of the same ground area. Our approach combines the computation of Range-Doppler surfaces and a variable target velocity backprojection SAR method. Potential targets are detected using multiple backprojection images and features are extracted using adaptive mixture models. We demonstrate the feasibility of our approach using target truth information provided with the Gotcha dataset. We outline the steps toward implementing a comprehensive automatic target tracking solution based on presented methodology.