Survey of approaches and experiments in decision-level fusion of Automatic Target Recognition (ATR) products

The US Air Force Research Laboratory (AFRL) is exploring the decision-level fusion (DLF) trade space in the Fusion for Identifying Targets Experiment (FITE) program. FITE is surveying past DLF approaches and experiments. This paper reports preliminary findings from that survey, which ultimately plans to place the various studies in a common framework, identify trends, and make recommendations on the additional studies that would best inform the trade space of how to fuse ATR products and how ATR products should be improved to support fusion. We tentatively conclude that DLF is better at rejecting incorrect decisions than in adding correct decisions, a larger ATR library is better (for a constant Pid), a better source ATR has many mild attractors rather than a few large attractors, and fusion will be more beneficial when there are no dominant sources. Dependencies between the sources diminish performance, even when that dependency is well modeled. However, poor models of dependencies do not significantly further diminish performance. Distributed fusion is not driven by performance, so centralized fusion is an appropriate focus for FITE. For multi-ATR fusion, the degree of improvement may depend on the participating ATRs having different OC sensitivities. The machine learning literature is an especially rich source for the impact of imperfect (learned in their case) models. Finally and perhaps most significantly, even with perfect models and independence, the DLF gain may be quite modest and it may be fairly easy to check whether the best possible performance is good enough for a given application.