  In this paper, we study a Target-Attacker-Defender (TAD) differential game
involving one attacker and one target, both with unlimited visibility range,
and multiple defenders with limited visibility capabilities. We assume that the
target and attacker are unaware of defenders' visibility constraints which
results in a dynamic game with asymmetric information. We seek to obtain the
strategies that are likely to be used by the players. The visibility
constraints of the defenders induce a visibility network which encapsulates the
visibility information during the evolution of the game. Based on this
observation, we introduce network adapted feedback or implementable strategies
for the defenders. We construct a class of parametric performance indices for
which the defenders' strategies along with standard feedback Nash strategies of
the attacker and target provides a network adapted feedback Nash equilibrium.
We introduce a consistency criterion for selecting a subset (or refinement) of
network adapted feedback Nash strategies, and provide an optimization based
approach for computing them. Finally, we illustrate our results with numerical
experiments.
