'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Situational understanding (SU) requires a combination
of insight — the ability to accurately perceive an existing
situation — and foresight — the ability to anticipate how
an existing situation may develop in the future. SU involves
information fusion as well as model representation and inference.
Commonly, heterogenous data sources must be exploited in the
fusion process: often including both hard and soft data products.
In a coalition context, data and processing resources will also be
distributed and subjected to restrictions on information sharing.
It will often be necessary for a human to be in the loop in SU
processes, to provide key input and guidance, and to interpret
outputs in a way that necessitates a degree of transparency
in the processing: systems cannot be “black boxes”. In this
paper, we characterize the Coalition Situational Understanding
(CSU) problem in terms of fusion, temporal, distributed, and
human requirements. There is currently significant interest in
deep learning (DL) approaches for processing both hard and
soft data. We analyze the state-of-the-art in DL in relation to
these requirements for CSU, and identify areas where there is
currently considerable promise, and key gaps