Metron, Incorporated SBIR Phase I Award, July 2019

Anti-aircraft artillery (AAA) pose a significant threat to military aircraft such as the F-35 and AC-130, as well as unmanned aerial vehicles (UAVs). The goal of this effort is to develop a knowledge-aided Bayesian inference algorithm that uses the available radar sensors on board the aircraft along with prior knowledge about AAA physical characteristics and topographical constraints to detect and locate AAAs. In Phase I, we propose to develop and implement a knowledge-aided Bayesian filtering algorithm that computes the probability of target presence (PTP) on a grid of resolution cells on the ground. The algorithm will be implemented in Matlab and will be capable of fusing information from a variety of sources, including detections from an active radar sensor, target identification (ID) calls from a SAR automatic target recognition (ATR) sensor, and ground position estimates from an AAA shell backtracking process. We will develop high-level statistical AAA radar models that characterize the unique RCS features of the AAA for discrimination against clutter, and take into account the type of clutter in each resolution cell. At the end of Phase I, we will provide a proof-of-concept demonstration using simulated data and a methodology for evaluating performance as a function of radar