Qualia centric hypothetical thinking: applications to vehicle tracking with the fusion of EO and SAR input data sources

Abstract

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.