Radar Debris Algorithms and Models for Discrimination

MARK Resources proposes to adapt BMD discrimination technology developed under MDA funding to reject debris while using greatly reduced radar resources. We will measure the positions, motions, and properties of scatterers on each object; derive the size of each object and the configuration of its scatterers, assuming it is a cone; and reject objects too small to be an RV, or whose scatterer configuration does not correspond to an RV. The proposed method offers important advantages over statistical approaches: debris rejection based on physical features is much more reliable; the algorithms do not depend on detailed modeling of debris and its statistical features, so do not require detailed information on the debris; radars of varying capabilities, including those without high signal bandwidth, can employ our method; and no higher signal-to-noise ratio is needed than that required for detection. The debris pre-screener should be demonstrated on measured radar data, supplemented by much more extensive simulated data. We have extensive experience in deterministic modeling of radar targets and in developing statistical models of a wide variety of clutter. We propose to adapt these models and procedures to generate high-fidelity coherent wideband signatures representative of debris present in a threat cloud.