In this work, we present a novel improvement to classical vehicle tracking algorithms by implementing a three-tier
architecture consisting of a data-centric vehicle tracker paired with a hypothetical thinking layer that is controlled by an
overarching goal layer – this models more effectively how a human thinks about and analyzes situations like vehicle
tracking. The upper two layers are disassociated from the data itself and instead operate from the idea of qualia in event
space. Our proof-of-concept results show how a classical vehicle tracker can be improved by fusing multiple input
sources using coincident SAR and EO data paired with a thinking layer that is able to detect, hypothesize, and resolve
conflicts.
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